gm2100 synthesis project...gm2100 synthesis project final report authors: bas altena e rosyni bou...
TRANSCRIPT
GM2100 Synthesis projectFinal report
Authors:
Bas AltenaEffrosyni Boufidou (final editor)Tom Commandeur (final editor)
Marjolein KoudijsSimeon Nedkov (final editor)Efstratios Tsompanopoulos
Martin ValkAmirpasha Vazifehdoust
Yijing WangHoe-Ming Wong
Daniel XuYe Yuan
Preface
The synthesis project is an elemental part of the MSc Geomatics program. The students usetheir knowledge on positioning technologies, location-based services, 3D modeling and geo-information systems in all different phases of the geo-information chain. A learning objectivefrom an educational point of view is the valuable experience of project management and team-work within the OTB institute of the TU Delft.
This year, the project contributes to the Climate City Campus project at the TU Delft. Thestudents are split in two teams which have delivered tools for climate simulation studies onthe TU Delft campus. The first team has investigated the possibilities of continuous trackingtechnology to support a sensor network on the TU Delft campus. The second team has createda 3D model of the TU Delft campus which is suitable for the use of climate simulation andtherefore contains both spatial and temporal elements. This project’s results aim to form apart of the framework for the Climate City Campus project. Based on the use of technicalterms, the reader is expected to have some technological background, however the terms andapproaches related to project management are explained in more detail.
We would like to thank our tutors, Ir. Verbree, Dr. ir. Tiberius, Mw. dr. dipl.-ing. Zlatanovaand Dr. ir. Ben Gorte, for the guidance and input on the project and documentation. Alsowithout the great help of the consultants this report would be non-existent. Therefore a wordof gratitude towards all consultants, Prof. dr. ir. van de Giesen, Prof. dr. ir. Russchenberg, Ir.Salcedo Rahola, Dr. dipl.-ing. Kenjeres, Dr. ir. van der Spek and Ir. Lesparre. Special thanksgoes out to Lumiad which made available software, hardware and knowledge. For the facilitiesand ICT support we thank the Faculty of OTB and the ICT Shared Service Centre. Last butnot least we would like to thank all others who contributed to this project.
TU Delft, 19th of October 2010
Bas AltenaEffrosyni BoufidouTom CommandeurMarjolein KoudijsSimeon NedkovEfstratios TsompanopoulosMartin ValkAmirpasha VazifehdoustYijing WangHoe-Ming WongDaniel XuYe Yuan
I
II
Contents
1 Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Aim of the project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Urban Climate Research at the TU Delft 32.1 Defining climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Defining urban climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 Enabling urban climate research . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3.1 Climate parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Project management 113.1 DSDM project approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.3 Requirements analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 Building a spatio-temporal aware sensor network 154.1 Sensing requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.1 MUST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.1.2 SHOULD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.1.3 COULD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.1.4 WON’T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Sensor network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.2.1 Sensors for climate research . . . . . . . . . . . . . . . . . . . . . . . . . . 184.2.2 Available sensors at the TU Delft campus . . . . . . . . . . . . . . . . . . 21
4.3 Positioning systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.3.1 Positioning techniques applicable in campus climate research . . . . . . . 214.3.2 Technique trade-off . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.3.3 Limitations of the chosen positioning combination . . . . . . . . . . . . . 284.3.4 Implementation of continuous positioning in the TU Delft campus . . . . 31
4.4 Integration of tracking and sensing . . . . . . . . . . . . . . . . . . . . . . . . . . 364.4.1 Measurement methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 364.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.6 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5 Building a 3D environment for urban climate research 455.1 Requirement analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.2 Available models, data sources and storage methods . . . . . . . . . . . . . . . . 47
5.2.1 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475.2.2 Available data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495.2.3 Storage formats and methods . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.3 Reconstruction and storage methodology . . . . . . . . . . . . . . . . . . . . . . . 545.3.1 Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.3.2 Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
III
5.3.3 Terrain and landuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605.3.4 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.6 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6 Conclusions 67
A Sensing 73A.1 Positioning combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73A.2 Blind spots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74A.3 Python combination algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75A.4 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
A.4.1 Tablet PC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81A.4.2 Arduino board and temperature sensors . . . . . . . . . . . . . . . . . . . 82A.4.3 Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
A.5 Integration Python code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83A.6 Indoor positioning systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
A.6.1 Indoor MEssaging System (IMES) . . . . . . . . . . . . . . . . . . . . . . 84A.6.2 Pseudolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
A.7 GPS mounted on an Arduino board . . . . . . . . . . . . . . . . . . . . . . . . . 85
B 3D modelling 87B.1 CityGML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
B.1.1 Vegetation Object Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 87B.1.2 Landuse model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89B.1.3 Generic CityObject . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89B.1.4 DTM storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
B.2 Tree parameters deriviation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92B.3 Terrain processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
B.3.1 Selecting parts of the AHN2 dataset . . . . . . . . . . . . . . . . . . . . . 94B.3.2 Create TIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94B.3.3 Convert TIN to suitable CityGML format . . . . . . . . . . . . . . . . . . 94
IV
Symbols and abbreviations
3G (3rd Generation) International Mobile Telecommunications-2000A-GPS Assisted GPSAHN Actueel Hoogtebestand NederlandALS Airborne Laser ScanningAP Access PointB-reps Boundary representationsCAD Computer-Aided DesignCCC Climate City Campus projectCDGPS Carrier phase Differential GPSCOM Component Object ModelCRU Climate Research UnitCSG Constructive Solid GeometryDC Direct CurrentDCA Dynamic Channel AllocationDGPS Differential Global Positioning SystemDOP Dillution Of PrecisionDSDM Dynamic System Development MethodDTM Digital Terrain ModelDXF Drawing Exchange FormatECEF Earth-Centred, Earth-FixedEPE Ekahau Positioning EngineEPSG (EPSG code) European Petroleum Survey GroupESRI Environmental System Research InstituteESS Ekahau Site SurveyEWI Faculty, Elektrotechniek, Wiskunde en InformaticaGBKN Grootschalige Basiskaart NederlandGGA Fix data, providing 3D location and accuracy dataGIS Geo Information SystemsGM Geometrical ModelGML Geography Markup LanquageGNSS Global Navigation Satellite SystemsGPGGA Global Positioning System Fix DataGPS Global Positioning SystemGSM Global System for Mobile communicationsHDOP Horizontal Dilution of PrecisionHSGPS High Sensitivity GPS
V
ICSP In-Circuit Serial ProgrammingIEEE Institute of Electrical and Electronics EngineersIMES Indoor MEssage SystemINS Inertial Navigation SystemIPCC Intergovernmental Panel on Climate ChangeIR InfraredISO International Organization for StandardizationJAXA Japan Aerospace Exploration AgencyKML Keyhole Markup LanguageLAI Leaf Area IndexLAN Local Area NetworkLBS Location Based ServicesLIDAR LIght Detection And RangingLOD Level Of DetailLOS Line-of-SightMIMAQ Company,Mobile Individual Measurements of Air QualityMGP Mathematical Geodesy and PositioningNDVI Normalized Difference Vegetation IndexNIR Near InfraredNLOS Non-Line-of-SightNMEA National Marine Electronics AssociationNN Nearest NeighborOGC Open Geospatial ConsortiumPAI Plant Area IndexPC Personal ComputerPRN Pseudorandom NoisePWM Pulse Width ModulationSD card Secure Digital cardSHP ShapefileSI International System of UnitsSPI Serial Peripheral InterfaceSQL Structured Query LanguageTIN Triangulated Irregular NetworkTM Topological MethodTPC Transmit Power Control
VI
UHI Urban Heat IslandUART Universal Asynchronous Receiver/TransmitterUBL Urban Boundary LayerUCL Urban Canopy LayerUHII Urban Heat Island IntensityUML Unified Modeling LanguageUSB Universal Serial BusUTC Coordinated Universal TimeUV UltravioletUWB Ultra-WidebandVRML Virtual Reality Modelling LanguageWAI Wood Area IndexWi-Fi Wireless FidelityWGS World Geodetic SystemWMO World Meteorological OrganizationXML Extensible Markup LanguageQGIS Quantum GISQZSS Quasi-Zenith Satellite SystemRAM Random-Access MemoryRD Rijksdriehoek, Dutch coordinate systemRF Radio FrequencyRFID Radio-Frequency IDentificationRSS Received Signal StrengthRSSI Received Signal Strength IndicatorRTLS Real Time Location System
VII
VIII
List of Tables
3.1 Stakeholder identification based on their activity. Each stakeholder requirementhas been coupled with a type of climate parameter as discussed in section 2.3.1 . 13
4.1 Identification of strengths and weaknesses of several positioning techniques com-binations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 Number of access points in the experimental area . . . . . . . . . . . . . . . . . . 314.3 Coordinate transformations from local to national coordinate system. . . . . . . 35
5.1 Tree species and their coefficent Mayhead (1973) . . . . . . . . . . . . . . . . . . 56
IX
X
List of Figures
2.1 Representation of the horizontal scales Oke (2004) . . . . . . . . . . . . . . . . . 4
2.2 Representation of the vertical scales Oke (2004) . . . . . . . . . . . . . . . . . . . 5
2.3 Urban Heat Island graphical representation Rahola et al. (2009) . . . . . . . . . 7
3.1 Rich picture of the Synthesis Project . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.1 Fixed and mobile sensors (a. fixed weather station, b. moving temperaturesensor, c. MIMAQ moving sensor box for air quality measurements) . . . . . . . 19
4.2 Causes and effects of air pollution: (1) greenhouse effect, (2) particulate contam-ination, (3) increased UV radiation, (4) acid rain, (5) increased ozone concentra-tion, (6) increased levels of nitrogen oxides Wikipedia (2010d) . . . . . . . . . . . 20
4.3 Delft Skyplot, with 10 degree cut-off angle over a 24 hour period, from the GNSSToolbox. Although the skyplot is made in the year 2000, the distribution is stillthe same. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.4 Calculating satellite availability in OTB building, using the line-of-site from apoint to the satellites Verbree and Zlatanova (2007) . . . . . . . . . . . . . . . . 29
4.5 Satellite availability around OTB building Verbree and Zlatanova (2007) . . . . . 30
4.6 First measurements in the ground floor of the OTB building indicating the ac-curacy of the Wi-Fi for positioning. ESS was used for the fingerprinting. . . . . . 31
4.7 First measurements in the ground floor of the OTB building indicating the cov-erage of the Wi-Fi network during fingerprinting with the ESS. . . . . . . . . . . 32
4.8 First measurements in the ground floor of the OTB building indicating the qualityof the Wi-Fi network signal during fingerprinting with the ESS. . . . . . . . . . . 33
4.9 Pixel coordinate system for OTB floorplan . . . . . . . . . . . . . . . . . . . . . . 34
4.10 GPS,Wi-Fi combination algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.11 Arduino Duemilanove I/O board Arduino . . . . . . . . . . . . . . . . . . . . . . 37
4.12 Low voltage analogue temperature sensor TMP36GT9Z boards (2010a) . . . . . 38
4.13 Arduino and temperature sensor in a sensor box boards (2010b) . . . . . . . . . 38
4.14 Positioned temperature measurements stored in the 3D model’s database . . . . 40
4.15 Temperature measurements visualised on a 2D model of the OTB building . . . . 40
4.16 Quality of the measurements together with their elevation attribute . . . . . . . 41
4.17 Wi-Fi accuracy statistics around the OTB building . . . . . . . . . . . . . . . . . 41
4.18 Satellite availability at 320 random locations in TU Delft campus (Verbree andZlatanova, 2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.1 Scheme for modelling the real world . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.2 Modelling a teapot using voxels (Washington University) . . . . . . . . . . . . . . 48
5.3 Modelling an object using CSG . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.4 Modelling building using B-reps . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.5 FLI-MAP system in action (Fugro, 2010) . . . . . . . . . . . . . . . . . . . . . . 50
5.6 A cross-section of AHN2 data from around the TU library, trees are clearlyidentifiable, as is the spike of the library. . . . . . . . . . . . . . . . . . . . . . . . 50
XI
5.7 An overview of the CityGML hierarchy. Every object stored in CityGML has ageometry (described in the Spatial Model), an appearance i.e. surface properties(described in the Appearance Model) and is of certain type (described in theThematic Model). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.8 The shape of the reconstructed trees is represented by their convex hulls . . . . . 575.9 NDVI raster value map around OTB, the blue circles indicate the tree locations. 575.10 The extended Vegetation Model UML diagram. The class Vegetation Additional Para
has been added . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585.11 The representation of a tree in LOD2. . . . . . . . . . . . . . . . . . . . . . . . . 585.12 Subset of AHN2-terrainfile covering the Mekelpark. . . . . . . . . . . . . . . . . . 615.13 Objects stored in the database . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
A.1 The algorithm for satellite availability spots definition . . . . . . . . . . . . . . . 74A.2 GPS shield mounted on an Arduino board. . . . . . . . . . . . . . . . . . . . . . 86A.3 The output of the GPS module when mounted on an Arduino board. The format
of the output is in standard NMEA and the message is a GPGGA message withall the information about time, location, accuracy, e.t.c. . . . . . . . . . . . . . . 86
B.1 Involved Vegetation model database tables. . . . . . . . . . . . . . . . . . . . . . 87B.2 Involved Land Use model database tables. . . . . . . . . . . . . . . . . . . . . . . 89B.3 Data format of the measurements as received from the tracking/measuring devices 90B.4 Tables involved when storing measurements. . . . . . . . . . . . . . . . . . . . . . 90B.5 Tables involved when storing a DTM in CityGML . . . . . . . . . . . . . . . . . 91B.6 The flimap system, resolution, beam angle and footprint. . . . . . . . . . . . . . 92B.7 Relation between wood area index, leaf area index and plant area index. . . . . . 93
XII
Executive Summary
Nowadays more and more people live in urban areas. To accomodate this influx of people,urban areas increase in size. Some cities have grown to tremendous sizes. As a city grows insize it starts to develop a climate of its own. Urban climate gives rise to several phenomenaone of which is the Urban Heat Island (UHI). UHI describes the phenomenon of cities havingan increased air temperature compared to the rural areas surrounding them. One of the conse-quences of a higher urban temperature is an increase in energy consumption. Several differentmethods exist which can be used to mitigate the effects of the UHI. For instance, vegetationhas a cooling effect on its surroundings. Therefore planting trees and developing green areaswill reduce the air temperature in a neighbourhood. Another method to reduce temperaturewould be to paint buildings with highly reflective paints such that the majority of received sunradiation is reflected.
However, most of these mitigation methods are only theories at best. Implementing them iscostly and time consuming. It is therefore convenient to have means to analyse the effects ofcertain measures prior to implementing them. Urban planners and climate researchers needa system which will enable them to analyse the effects of heat mitigation measures and moregenerally, to be able to perform urban climate analysis. The Geomatics Synthesis Project haslaid the basis for such a system.
The Geomatics Synthesis Project is dedicated to the TU Delft Climate City Campus (CCC)project. The CCC’s aim is to turn the TU Delft campus into a showcase for multidisciplinaryenvironmental research. The goal is to make the TU Delft campus one of the best monitored andunderstood, in terms of urban climate, campuses in the world. To help to achieve this goal twotools have been constructed during the Synthesis Project. First, a sensor tracking system formobile and fixed sensors is built which is designed to sense a large number of climate parameters(air temperature, air humidity, wind velocities, etc.). Second, a 3D framework is constructedwhich contains a representation of all features (buildings, streets, trees, grass, waterbodies, etc.)found on the campus. This 3D framework will be used as a centralized storage place for themeasurements measured by the sensor network.
The first goal of the Synthesis project therefore is to
• build a sensor tracking system for continous sensor tracking, which is part of a sensornetwork, in the built environment
Traditionally, tracking is performed by GPS-like systems. However, such tracking systems tendto have difficulties in urban environments since their signals are blocked and scattered by cityobjects. To solve this issue, a study has been performed on the usability of other signalsinherent to the urban environment (such as television signals, mobile network signals, wirelessnetwork signals, etc.) to act as enhancement for the GPS signals or to act as standalonepositioning systems. After making a trade-off between different technologies, the combinationGPS + Wi-Fi was deemed most suitable due to the high availability of Wi-Fi access spots(especially on campus), the low cost and claimed relatively high accuracy (3-5 meters) of theused Wi-Fi positioning software Ekahau RTLS. Wi-Fi positioning is to be performed by way offingerprinting.
XIII
To demonstrate the tracking system, a blind spot analysis has been performed. Blind spots arelocations on campus where estimation of position is not possible due to loss of signal. When theuser enters a blind spot the system automatically chooses the best positioning method: Wi-Fior GPS.
To demonstrate the sensing capabilities of the system, a temperature sensor has been assembledand linked to the positioning system. As such, measurements can be taken anywhere, anytimeas a function of time and location.
In view of the CCC goals stated above, the second goal of the Synthesis Project is to
• build an extensible and solid framework which is able to store and handle 3D representa-tions and thematic properties of the built environment and the measurements made therein
The 3D framework was made fit for climate research by allowing it to store urban climateparameters. A distinction is made between dynamic and static climate parameters. Dynamicclimate parameters vary frequently in time. They are a function of local weather. Air temper-ature and humidity are examples of dynamic climate parameters. Static climate parameters,on the other hand, vary only slightly in time. They are a function of the static environment(buildings, roads, etc.). Surface properties such as roughness and reflectivity are examples ofstatic climate parameters.
CityGML has been chosen as the data storage model. CityGML is an XML-based, object-oriented open data model which can store geometry and thematic properties of urban objects.In this case, thematic properties and static climate parameters are deemed equal. All data isstored in a spatial relational database.
Trees have received considerable attention in the 3D framework since they have significantimpact on their surroundings, climate-wise. The CityGML model has been extended to acco-modate the tree properties canopy density and Leaf Area Index. These have been extractedfrom available laser scan data.
By combining both parts of the Synthesis Project, the CCC project will have a 3D frameworkat its disposal which will allow scientist from different fields to perform seamless measurementsand store these in a centralized location while having access to 3D data of the campus.
XIV
Chapter 1
Introduction
1.1 Motivation
An ever increasing number of people is living in urban areas. It is only recently that the numberof people living in cities surpassed that of people living outside of cities. This in turn results inever growing urban areas. As cities grow in size their influence on the environment increases.Urban areas are not just at the mercy of climate, rather, they spur their own climate.
Research on urban climate is growing but difficult to perform due to the complexity of urbanareas and lack of sensors in the built environment. One of the ways TU Delft is going tocontribute to urban climate research by way of the Climate City Campus project.
The Climate City Campus (CCC) project has the goal to make the TU Delft campus a showcasefor multidisciplinary environmental research. The TU Delft campus is an excellent test bed forurban climate research since it is relatively compact yet diverse enough to closely represent anaverage Dutch city.
The CCC project’s objective is to make the TU Delft campus one of the most thoroughly sensedand best understood campuses in the world. The Geomatics Synthesis project will contributeto the foundation needed to achieve this goal.
1.2 Aim of the project
The aim of the Climate City Campus is to enable multidisciplinary environmental researchon the TU Delft campus. According to Oke (2006) performing climate research comprisesthe following stages: conceptualization, theory, observation, modelling, validation, applicationand evaluation. The Geomatics Synthesis project deals with the observation and modellingstages. First, a method will be developed which will enable the seamless (anytime, anywhere)measurement of urban climate parameters. Second, a centralized information system will bebuilt that enables the efficient storage and management of the sensed data along with 3Drepresentations of the built environment.
Urban climate is a spatio-temporal entity. Its parameters vary not only with time but also withlocation i.e. different parts of a city might experience different temperatures at the same timedepending on the distribution of buildings, parks, trees, etc. Gathering urban climate datatherefore requires a sensor which is able to take measurements as a function of time but also asa function of position i.e. a mobile platform with sensors. However, tracking objects in urbanareas is somewhat challenging. In general, Global Navigation Satellite Systems (GNSS) suchas GPS are of limited use in urban areas since their signals are unable to propagate throughbuildings, trees, etc. Built environments, however, tend to be covered by a number of other
1
signals (mobile telecommunication signals, television signals, wireless internet networks) whichcan be used as a replacement or enhancement of GNSS signals such that tracking in urban areasis still possible. Therefore, the first goal of this project is to
• build a system to continously track sensors in a sensor network in the built environment
Climate is a three dimensional phenomenon i.e. the temperature at street level is different fromthe temperature at roof level. The information system must be able to easily accomodate thethird dimension. Dealing with urban climate is challenging due to the complexity of the builtenvironment. Urban areas harbor many different types of objects, each with its own complexgeometry and thematic properties. The storage method used in the information system mustbe capable of handling these issues in an efficient manner. Taking the stated considerations intoaccount, the second goal of this project is to
• build an extensible framework which is able to store and handle 3D representations andthematic properties of the built environment and the measurements made therein
By meeting the stated goals, the Geomatics Syntehsis Project will provide the CCC with aframework which will enable researchers from different fields to store and retrieve measurementsof static and climate parameters recorded anywhere on the campus in a centralized informationsystem.
2
Chapter 2
Urban Climate Research at the TUDelft
2.1 Defining climate
Climate in a narrow sense is usually defined as the “average weather”, or more rigorously, thestatistical description in terms of the mean and variability of relevant quantities over a periodof time ranging from months to thousands or millions of years. The classical period is 30 years,as defined by the World Meteorological Organization (WMO). These quantities are most oftensurface variables such as temperature, precipitation, and wind. Climate in a wider sense is thestate, including a statistical description, of the climate system Parry (2007).
On a global scale, climate is most affected by latitude, the tilt of the Earth’s axis, the movementsof the Earth’s wind belts, the difference in temperatures of land and sea, and topography.However, the effect of these factors is evident in different scales of observation and that is whatmakes the climate research a complex task.
A region’s climate is defined as the general or average weather conditions of that certain regionat a certain time interval, including temperature, rainfall, and wind. Human activity, i.e.constructions, energy consumption and the depletion of the ozone layer, is another importantfactor to investigate during climate research. Through climate research, analysis of the causesand practical consequences of climatic differences and changes related to a location will becarried out Ame (2010).
Before starting a study about climate and climate control in a region, the observation environ-ment has to be defined. Such a process will conclude in correct monitoring and modelling of theregional climate. The observation scale together with the surrounding characteristics and thespecificities of a region, are in fact those that control the climate in that region. The observationscale has a definition in both the horizontal and the vertical direction.
Horizontal scale The term horizontal scale in climatology refers to the horizontal distancein which a phenomenon affects an area as well as in the width of the area investigated. Thesedistances are very important for the study of weather and climate phenomena and are definedroughly with three scales (see figure 2.1)
• Microscale The microscale climate is constrained in the area around the buildings inan urban environment. Every surface and object has its own microclimate in its vicinity.Surface and air temperatures may vary by several degrees in very short distances, upto millimeters, and airflow can be greatly perturbed by small objects. Typical scales ofurban microclimates relate to the dimensions of individual buildings, trees, roads, streets,
3
courtyards, gardens, etc. This scale extends from one meter to hundreds of meters.
• Local scale The local scale climate refers to the scale that standard climate stationsare designed to monitor. It includes landscape features (i.e. topography) but excludesmicroscale effects. In urban areas this includes the climate of neighborhoods with similartypes of urban development (surface cover, size and spacing of buildings, activity). Thesignal is the integration of a characteristic mix of microclimate effects arising from thesource area in the vicinity of the site. Typical scales are one to several square kilometers.
• Mesoscale Mesoscale measurements refer to weather and climate measurements influ-enced by the city and at the scale of a whole city. It extends typically to tens of kilometersand a single station is not able to represent this scale.
Figure 2.1: Representation of the horizontal scales Oke (2004)
Vertical scale In the vertical axis, the layers formed above an urban area can be distinguished.Figure 2.2 illustrates the urban atmosphere and its division in the vertical axis. In a ruralenvironment or in an environment with low level of urbanism, the energy and moisture exchangestake place in nearly planar surfaces. However, in an urban environment these exchanges aretaking place in a layer of significant thickness, the Urban Canopy Layer (UCL). The UCL is inthe bottom of the Urban Boundary Layer (UBL). UCL’s height is approximately equivalent tothe mean height of the roughness elements of the urban environment (buildings and trees) and itis the layer in which the urban climate phenomena take place. The microclimatic characteristicsof the surfaces persist for short distance from their source and are then mixed and muted bythe action of turbulent eddies, i.e. air or water flows.
The blending occurs in both the vertical and the horizontal direction. As noted, the horizontaleffects may persist up to a few hundred meters whereas for the vertical axis, the effects of theindividual features are limited to UBL. The blending action is complete at the top of the UBL.
The height in which UBL can be found differs between the urban environments. Empiricalknowledge has shown that in densely built sites the UBL can be found at 1.5*(UCL height)heights whereas in low density areas can be found at heights up to 4*(UCL height).
4
Figure 2.2: Representation of the vertical scales Oke (2004)
Therefore, what is important to take into account is, that a sensor network placed below theheight of the UBL can be affected by microclimate anomalies whereas above that level, themeasurements recorded are representative of a well-blended and spatially- averaged atmosphereand can be indicative for the local scale climate.
The Climate City Campus project’s goal is to make the TU Delft campus a test bed for urbanclimate research. The campus is a region of several square kilometres that is influenced by localscale and microscale phenomena. Global scale phenomena are of less interest in this project.As far as the vertical direction is concerned, weather conditions met in the UBL and the UCLare of interest. Since the focus lies on urban areas a more detailed definition of urban areas andtheir climate is needed and given in the following section.
2.2 Defining urban climate
Urban climate refers to climatic conditions in an urban area that differ from neighbouring ruralareas and are attributable to urban development. Urbanization tremendously changes the formof the landscape and also produces changes in an area’s air (wikipedia reference).
Urban environments are characterised by a high level of construction and a high populationdensity. It is about areas that are expanding continuously and have experienced, in the lastyears especially, great development. The actual effect of urbanism and human activity to urbanclimate is not very well understood. This lack of understanding is caused by the complexity ofclimate research in general, but also by the fact that the area of interest is in itself complex (i.e.the built environment).
When performing urban climate research, the investigation is constrained to an area of severalkilometres, i.e. the city centre conditions are important. Therefore the research concernsinfluence of local scale and microscale phenomena. Climate phenomena measured in the UBLon the other hand, are representative of the urban climate whereas moving deeper, i.e. insmaller heights, into the UCL, the microclimatic effects of the city objects can be identified.
Each region has its own characteristics and each actor in that region, i.e. topography, trees,building materials, roads, people, holds its own micro-climate that affects the atmosphericprocesses. The parameters affecting the region’s climate are interdependent and therefore evensmall changes in the surrounding area have an effect in the region’s climate. Therefore, theclimate formation in a region could be characterised as a dynamic and perpetual process thatderives its characteristics from the surroundings.
Building materials and road materials store solar heat which is emitted later as infrared heat
5
radiation. Cars traversing the city, emit gaseous pollutants, heat and noise through their en-gines. This pollution is caged in the city since building facades block the air currents comingfrom the rural areas and therefore the atmosphere is not renewed . The lack of vegetation andwater on the other hand, result in low evaporation levels that could decrease temperature. Theneed for comfort in such suffocating environments is evident and makes people consume moreand more energy, i.e. air cooling and heating systems in order to feel better. Another heatsource is then generated, one more pollution source is added to the list and the comfort in thecity decreases. The factors already explained as harmful for the urban climate are related toheat and the heating of the atmosphere. Rahola et al. (2009) in his research, refers to thermalcomfort to explain the people’s behaviour in urban environments.
Thermal comfort Thermal comfort refers to the state of mind that expresses satisfactionwith the surrounding environment Rahola et al. (2009). Thermal comfort is a very importantterm in urban climate research. It is related to human activity since humans are the ones whocan feel the climate and can react to climate changes. The need for thermal comfort leadspeople nowadays to act in a wasteful way that affects the urban climate. They consume moreenergy in order to feel more comfortable whereas at the same time they burden the atmospherewith one more heat source. It comes easily therefore, that in urban environments where there isa high concentration of people, the leading principle to achieve a balance in climate, is to ensurethermal comfort for people both indoors or outdoors without using extra energy but exploitingthe resources offered in the surroundings.
Four variables are proposed Rahola et al. (2009) as variables that are relevant to thermal comfortin an urban environment.
• air temperature
• water vapour pressure
• wind speed
• mean radiation temperature
Thermal comfort in an urban environment can be achieved by influencing the climate parametersin a clever way. For example, solar heat can be used more effectively if the building materialsused allow heat to disperse uniformly through the building. In such a way, heating and coolingsystems would be working properly and therefore less energy would be consumed. Furthermore,an efficient arrangement of buildings in densely built areas would create shades, useful both forelimination of the solar heat that reaches buildings as well as for urban planning purposes i.e.arrangement of a recreation area based on the thermal comfort.
Another very important factor that is relevant to thermal comfort is the evapotranspiration.Vegetation and water are the parameters needed to enable evapotranspiration in the atmosphere.In many urban environments there is a lack of trees, green areas and water. Their presencewould increase transpiration and evaporation and therefore the temperature would get lower.However, trees are not only useful as transpiration sources. Trees can work as filters for thegaseous pollutants i.e. CO2, NOx especially in areas with high car concentration i.e. along busyroads. Later, again through photosynthesis, CO2 can be released as oxygen by the trees.
Wind could also be used to improve thermal comfort in urban environments. If the buildingswere not blocking fresh air coming from the rural areas then the urban atmosphere would behealthier and the temperature would be lower. Keeping wind away from urban environmentscan cause temperature inversions i.e. an increase in temperature with height, which leads topollution, such as smog, trapped close to the ground.
The Urban Heat Island effect The above-mentioned factors affect the urban climate andtheir influence is evident in modern cities. Nowadays, the most important effect of urban climate
6
change is expressed through the Urban Heat Islands. An Urban Heat Island (UHI) is an urbanarea that experiences higher temperatures than its surroundings. This effect is more significantduring the night and can have values up to 10oC.(See figure 2.3)
Figure 2.3: Urban Heat Island graphical representation Rahola et al. (2009)
The reason for such a gap in maximum temperatures between the city and the countryside,is the accumulation of heat in cities by means of caging of solar heat in materials (based ontheir thermal capacity) and gradual release as emittance of longwage radiation while coolingdown at night Rahola et al. (2009). The urban factors listed in the previous paragraph asfactors that decrease the comfort in urban environments are evident in an UHI with emphasisin temperature, wind, vegetation, water and gaseous pollutants.
2.3 Enabling urban climate research
The Urban Heat Island phenomenon gives rise to several effects the most profound one being anincrease in energy consumption, especially during summer. The higher temperature of the cityresults in an increased use of air conditioners. This results in an increase of energy demand inthe order of magnitude of 1500MW per 2.5◦C increase in temperature as calculated by Akbariet al. (2001) for 1920’s downtown of Los Angeles. To be able to battle this rise in temperature,city planners need a better understanding of urban climate. Rahola et al. (2009) propose sometechniques for the mitigation of the effects of the UHI.
• reduce the number of surfaces exposed to solar radiation in the city, by building denselyand orientating buildings carefully.
• reduce the heat absorbed in the city by increasing reflection levels (albedo) or solar energyconversion.
• improve natural cooling mechanisms (city and building ventilation, evatranspiration byvegetation and open water.
Action to reduce the effects of urban climate and the UHI can be taken on different levels. Onemay focus on single buildings by, for instance, covering them with green roofs, painting themwith high reflectivity paints, equipping them with heat pumps, etc. The same measures can beintroduced to whole neighbourhoods. At this level it becomes relevant to look at the effect oflarge green areas (parks, groups of trees, etc), large bodies of water and groups of buildings (interms of their orientation and their shadowing of one another). Yet a different approach is to
7
take administrative measures (introduce subsidies orchange legislation) or change the habits ofurban dwellers.
However, the implementation of many of these measure will require a significant investmentof time, knowledge, people and finances. It is therefore necessary for urban planners, cityauthorities and engineers to have a system which enables them to understand, predict andevaluate the effects of a measure. An urban planner might be interested in the temperaturedistribution of a neighbourhood before actually building the neighbourhood. An engineer mightbe interested in knowing what is more effective, painting a roof in a different colour or making ita ”green” roof. More close to home, researchers at the TU Delft may be interested in performingwind and heat simulations. Measurements of the actual situation are needed to validate thesimulations.
The Geomatics Synthesis project’s goal is to design and deliver this system. But before such amethod is designed, it is importan to identify the parameters which influence the urban climate.
2.3.1 Climate parameters
Based on the previous two sections, the parameters which define the urban climate can be splitin two distinct groups: dynamic parameters and static parameters. Dynamic parameters arecharacterized by a high variability i.e. a relatively high rate of change. Dynamic parametersinclude temperature, air humidity, wind velocities, etc. Dynamic parameters are measurablequantities. Static parameters, on the other hand, have a low rate of change. Static parame-ters include surface properties, the location and orientation of buildings, location and size ofvegetation and water bodies, etc.
Both dynamic and static climate parameters have an influence on the urban climate. Forinstance, a black (static parameter) building absorbs more heat during the day than a whitebuilding. This heat, when released by the building at night, will result in a rise of the localtemperature (dynamic parameter).
Dynamic urban climate parameters Following is a list of dynamic climate parameterswhich, based on the previous two sections, are deemed important and will be taken accountwhen designing the urban climate analysis tool. Dynamic parameters are in general quantitieswhich can be measured or sensed.
• Air temperature - temperature has an influence on thermal comfort of people.
• Object temperature - needed for modelling radiation levels.
• Wind - influence, for example, the air temperature and air pollution: a lack of wind mayresult in higher temperatures and polluted air. Buildings may block wind and cause sameeffects as a no-wind situation even when there is wind.
• Precipitation - Control of the amount of water reaching the ground. Has the urban regionenough “free” ground to absorb precipitation or is it paved?
• Water leaving the system: evaporation, transpiration, sewerage, drainage channels
• Ground absorption - Definition of healthy grounds or grounds that need more water tosupport vegetation in transpiration and photosynthesis
• Air humidity - Influences comfort, but also evapotranspiration levels
• Air pollution - CO2 and NOx concentration monitoring, in relation to temperature andhumidity. Definition of the air quality and pollution
• Energy consumption - of buildings, in particular for cooling and heating purposes, is anindicator for the thermal insulation of a building but also about the habits of the people
8
occupying it. The energy that is used in a building will eventually heat up the surroundingof the building.
• People’s behaviour - monitoring people’s behaviour will provide information on their ther-mal comfort levels.
• Incoming/outgoing shortwave and longwave EM radiation, i.e. light and heat.
Static urban climate parameters Following is a list of static climate parameters. Staticclimate parameters are related to the urban environment.
• Surface properties - color, reflectivity, heat conductivity and heat capacity of surfacesdefine the reflected and absorbed amount of sun radiation.
• Building - the location, orientation and geometry of a building define its interaction withwind and sun radiation.
• Vegetation - has great impact on the urban client as it helps keep temperatures lowby transpirating. Trees furthermore provide shadows which in turn shield, for instance,concrete from heating. However, trees need to be kept in check since they may pose athreat to nearby buildings during storms.
• Waterbodies - also have an influence on the urban climate. Evaporating water extractsheat from its surroundings. Waterbodies therefore help cool the urban environment.
• Roof types - some roof types have favourable properties with regard to sun radiation. Aroof which reflects more radiation will stay cooler than a roof which reflects less.
• Building facade properties - facades covered in glass ...
Proposed urban climate analysis method According to Oke (2006), performing climateresearch comprises the following stages: conceptualization, theory, observation, modelling, val-idation, application and evaluation. As stated earlier, the Synthesis Project will focus on theobservation stage and the modelling stage. The observation stage will be adressed by
• building a sensor tracking system which is able to continously track a sensor, which is partof a sensor network, in the built environment
The need for continious tracibility arises in climate research from the fact that the dynamicclimate parameters vary not only with time, but also with location. This is best illustratedby way of a city fountain. The air temperature, at a certain time, near the fountain will belower than the temperature ten metres away from the fountain. Yet, these temperatures willbe different a couple of hours later.
Nowadays most outdoor tracking needs are fulfiled by a Global Navigation Satellite Systems(GNSS) such as GPS. However, GNSS systems have difficulties operating in urban environmentssince the tracking signals have difficulties propagating through buildings, trees, etc. This resultsin a position fix with a low accuracy or no fix at all. Fortunately, urban environments are coveredby other telecommunications signals (mobile networks, television signals, wireless networks, etc.)which can be used as supplements to GNSS signals or as standalone positioning systems.
The modelling stage will deal with both static and dynamic climate parameters. As indicatedabove, static climate parameters are properties of the real world i.e. surface parameters, thesize and orientation of buildings, trees, etc. The dynamic parameters are to be measured bythe sensor network. To integrate both dynamic and static climate parameters an
• extensible and solid framework will be built which is able to store and handle 3D repre-sentations and thematic properties of the built environment and the measurements madetherein
9
10
Chapter 3
Project management
This year’s Design Synthesis project was characterized by the large number of people involved.There were twelve students working towards meeting the requirements of eight stakeholders.The available time to meet these requirements was limited to eight weeks. In these eight weeksthe students had to quiclky grasp the problem at hand, talk to the stakeholders, design andimplement a solution based on the stakeholder’s requirements. Managing twelve students andworking together with eight stakeholders calls for solid project management. This chapter de-scribes how the project was managed, which stakeholders were involved and what requirementsthey have for the system. Once these requirements are found, a prioritization will take placewhich decide which requirements are to be implemented.
3.1 DSDM project approach
The Dynamic System Development Method (DSDM) was chosen as the project managementparadigm. DSDM’s goal is to deliver products on time time and on budget while taking changesin the system requirements into account. Two of DSDM’s main principles are applicable to theSynthesis project: user involvement and rapid product releases which are ”good enough”. Usersof the product are involved in the development of the product and therefore also responsiblefor the result. This combined with a ”release early, release good enough” mentality allows theend users to steer the process such that it fits their needs. On the other hand, since productsare released rapidly, the developers have many points in time in which they can incorporate thefeedback of the users. The DSDM paradigm is ideal for the Design Synthesis project since thereare eight stakeholders which need to be served and a complex product needs to be developed.
3.2 Stakeholders
The Synthesis Project is dedicated to urban climate research. More specifically, it is dedicatedto the Climate City Campus project. The Climate City Campus project is a TU Delft initiativewhich promotes and facilitates scientists and students from different disciplines to use the cam-pus as a test bed for urban climate research. To facilitate multidisciplinary research the CCCplans to deploy a centralized data storage and management infrastructure which can store andmanage all the data which is collected, analysed and produced by all urban climate researchersat the TU Delft. The CCC is a major stakeholder of the Synthesis project.
The users of said infrastructure are researchers and students who collect and analyse urbanclimate data. To better understand their needs, several researchers and experts from differentfields have been assigned as stakeholders to the Synthesis project. Table 3.1 identifies the
11
different stakeholders, their specific requirements for the system and the climate parameters, asidentified in chapter 2, which best correspond to the stakeholder’s requirements.
Maybe the
teams can start
with the
integration of
the rain gauges.
I want continuous
tracking of
sensors
I want to monitor
heat distribution
on the campus
Verbree Lasparre Rahola
v.d. Giesen
Russchenberg
v.d. Spek
Use simple
sensors or already
operating sensor
networks
Gorte
Zlatanova
Make sure the 3D
model is adaptable
for different
simulation
scenarios
We are coaching
the two teams
Tiberius
The TU Library has in-
house data and data
storage procedures
v.d. BergKenjeres
I can guide the
team in
streamlining their
3D model for
simulation
purposes
Make sure the
complexity of
the model is in
balance with
the simulation
needs
We are building an
extensible 3D model
which will allow the
seamless analysis of
climate data
We are working
on the continuous
tracking of
sensors in a
sensor network
I can track
anything! I'll help
the team track their
sensors.
CCC: we want a
system which can
track sensors and
display the
measurements in a
3D environment
Figure 3.1: Rich picture of the Synthesis Project
12
Stakeholder ac-tivity
climate param-eters of interest
Requirements on 3D model andsensing system
CCC organisation all climate param-eters defined insection 2.3.1
modelling: design an extensible 3Denvironment which can store andmanage urban objects and their the-matic properties. The 3D environ-ment is to also store climate param-eters. This 3D enviroment will serveas the backbone for future urbanclimate research at the TU Delft.Sensing: use mobile and static sen-sors, use existing data, focus onmeasuring one specific cilmate pa-rameter. Correlate weather condi-tions to signal quality, availability.Do not build hardware and installnew measurement stations. Keeplink between sensing and 3D mod-elling simple.
Heat simulations trees, surfaceproperties: re-flectivity, heatcapacity, rough-ness
allow the storage of surface param-eters, trees and other vegetation.Perform shadow analysis on treesand buildings. Focus on frequentedcity locations.
Wind simulations trees along witha measure for theporosity of thetree canopy, accu-rate building ge-ometry
storage of trees, the ability to eas-ily insert and extract buildings andother objects to/from the model.Allow for the visualization of sim-ulation results in the 3D model.
People trackingand comfort inthe built environ-ment
same as for heatsimulations
realize continous tracking of peoplefor the purpose of mobility research.Relate pedestrian movements to ur-ban conditions e.g. correlate routestaken by people to urban hot spots.
Synthesis projectsupervision
none synthesis of the knowledge gained inthe first year of Geomatics. Experi-ence all phases of product develop-ment.
Students none use standards as much as possible.Use available data as much as possi-ble. Investigate legal aspects of sys-tem. Create showcases of system ca-pabilities.
Table 3.1: Stakeholder identification based on their activity. Each stakeholder requirement hasbeen coupled with a type of climate parameter as discussed in section 2.3.1
13
3.3 Requirements analysis
The requirements analysis is done based on the MoSCoW method. MoSCoW is the prioritisationtechnique of the DSDM project management approach and is used to map the importancestakeholders place on the delivery of each requirement. The captial letters in MoSCoW identifythe different priority levels as
• M - MUST have this
• S - SHOULD have this if at all possible
• C - COULD have this if it does not affect anything else
• W - WON’T have this time but WOULD like in the future
MoSCoW is often used with timeboxing. Timeboxing is a time management technique whichdivides the toal amount of available time for a project in boxes. Each box is then given adeadline. The sum of all boxes results in a sucessful project. Each timebox has its own set ofMoSCoW requirements. Requirements placed on the MUST level are critical for the successfulcompletion of the project. As such, they must be implemented in all timeboxes which containthem. MUST requirements cannot be moved between timeboxes as they are deemed critical forthe succesful closure of a timebox. Requirements placed in the SHOULD level are also criticalfor the project but they are somewhat more flexible as they can be moved between timeboxes.Requirements at the COULD level are requirements which, when implemented, will make thecustomer happy at no or very low cost. They are however not critical for the success of theproject. Requirements at the WON’T level are not going to be implemented in the currentproject.
Sections 4.1 and 5.1 present the MoSCoW analysis of the sensing and 3D modelling components,respectively.
14
Chapter 4
Building a spatio-temporal awaresensor network
4.1 Sensing requirements
The need for monitoring climate parameters in an urban environment is evident from the previ-ous chapters. The urban climate has some specificities, it differs from neighbouring rural areasand is attributable to urban development. Urbanization tremendously changes the form of thelandscape and produces changes in the area’s air Wikipedia (2010e). These characteristics makethe urban climate research an interesting topic since they are priming the research around theactual effect of urbanization in urban environments. The difference in climate between urbanand rural areas, indicates not only weather parameters but also their relation to city objects asresponsible for the climate differences.
This dependence of the climate to place and time shows the spatio-temporal nature of urbanclimate research and urges the use of positioning technology along with sensor measurementsfor the acquisition of valuable data.
Monitoring of the temperature pattern in an area during a specific time interval, can show therelation of temperature with the surrounding area especially when the surface characteristicsare known. Furthermore, capturing together temperature and humidity measurements evenalong with gaseous pollutants concentration can add to the knowledge about air quality. Then,mitigation solutions can be found when the actors/objects of the surrounding area are known.
Towards the initiative of the CCC, several stakeholders are acquainted with mitigating and sus-tainable solutions for the campus. Based on the stakeholders’ prerequisites defined in section3.2, the requirements for a continuously trackable sensor network are defined. These require-ments are handled with a MoSCoW diagram. Through this diagram, the top level requirements,i.e. must and should, are distinguished by the the requirements that could or won’t be fulfilled.The MoSCoW diagram is a dynamic diagram since during the project’s time-line, the top levelrequirements may change, based on the project’s progress and products, and some of the re-quirements defined as “could be fulfilled” are moved to the top level requirements. The system’srequirements were defined after carefully examining the needs of the system in combination tothe stakeholders’ requirements. The following section presents our MoSCoW digram.
4.1.1 MUST
Use High Sensitivity GPS receivers High sensitivity GPS (HSGPS) receivers will coverthe needs for positioning in an environment with high signal-to-noise ratio and multipath(e.g. the TU Delft campus).
15
Use an alternative/complementary positioning technology Alternative positioning sys-tems are needed for the blind spots of the campus where not enough satellites are presentto get a position fix even with HSGPS receivers.
Use fixed and mobile sensors The use of fixed and mobile sensors data will enable completecoverage of the needs for continuous tracking of climate parameters in the university cam-pus. Fixed and mobile sensors data will act complementary. Furthermore, fixed weatherstations are usually equipped with more efficient instruments, very important during ur-ban climate research, that cannot be replaced by mobile sensors, e.g. anemometers, raingauges.
Implement positioning combination software In order to enable continuous positioningin the campus the positioning technologies used have to be combined so as to have coverageeverywhere. New software is then needed to cover this need.
Implement measurement tracking software After enabling continuous positioning in thecampus, climate parameters tracking has to be enabled to adhere in the needs of the CCC.
Survey the campus area and search for limitations The campus area has to be surveyedin order to define the limitations of the positioning systems. A map with the HSGPScoverage as well as a blind spots map are very important.
Keep the regional coordinate system Transformations over the reference coordinate sys-tems given by the positioning technologies will be needed. This can be done by transform-ing everything in a common coordinate system, specifically the Dutch coordinate systemRijksdriehoek new (RD new, EPSG 28992).
Meet educational goals as defined in the project guide.
4.1.2 SHOULD
Consider measurement and position accuracy Even though availability in positioning sys-tems is more important than accuracy, the capabilities of positioning systems as far asaccuracy is concerned are of interest. The complementary positioning system has to beaccurate enough to cover the needs for positioning as well as to cover the needs of theCCC project in climate parameters measurements. In urban climate research, accuracy isimportant since the research area has limited width and the microclimate of city objectsmatters. Having small accuracy, the effect of buildings or trees cannot be investigated.
Ensure a data communication stream A data communication stream is important in aninfrastructure for climate research. That would mean investigation of the communicationcapabilities between the sensors and the IBM system bus, i.e. Wi-Fi, 3G e.t.c.
Exploit existing harware Due to limited time and limited resources, the existing hardwarehas to be exploited to define the possibilities for positioning and climate research. Whenthe limitations of the existing hardware are defined, an investment on new hardware withspecific capabilities will be done.
Use existing datasets and plans The use of existing datasets and plans saves time from theimplementation process. Climate parameters measurements, e.g. temperature measure-ments, and floors plans of the buildings available in the TU Delft will decrease the effortand time needed for survey.
Consider privacy issues on people tracking The dynamic sensor network to be built pre-supposes that somebody will carry the sensor with him. Therefore, except for the positiontracking of a climate measurement, the person’s position tracking comes directly. Peopletracking is a sensitive issue nowadays and especially when the reason for tracking is not a
16
safety reason. Therefore, the CCC stakeholders have to think around the applicability ofa dynamic sensor network “carried” by normal people.
Consider privacy issues for communication The communication between the 3D modeland the positioning system has to adhere to the TU Delft’s network privacy rules.
4.1.3 COULD
Consider communication security Communication security between the devices has to beensured for a functional positioning system. Jamming and interference from other devicesmay fail the whole initiative.
Consider weather influence on the measurements Weather phenomena, e.g. barometricpressure, direct solar radiation, may influence both the position estimate and the climateparameters measurements. However, the actual effect is not yet defined and would dependon the receivers’ and sensors characteristics.
Pose a legal disclaimer A legal disclaimer will make the product creators non-liable to pos-sible problems concerning the use of this product, i.e. people’s privacy.
Multi sensor boxes Multi sensor boxes are designed to capture several measurements at thesame time. Even sensor boxes with the ability to capture position, i.e. with a GPS receivermounted, and weather parameter are available today in the market. This solution couldaccount for a more portable and compact solution.
4.1.4 WON’T
Build new hardware There is no time, during this project, to invest on building new hard-ware. However the project’s output can be useful to define the needs in hardware for theurban climate research.
Build a real-time system A real-time system building would need more time (than the avail-able) and very powerful servers.
Spend time for the communication between the 3D model and the sensor networkIBM is responsible for the enterprise service bus that will standardise communication be-tween different components of the CCC research infrascturcure. Therefore, the communi-cation between the 3D model and the sensor network can be limited to an experimentallevel.
Buy new software No new software will be purchased since the TU Delft’s resources have tobe exploited first. Furthermore, this constraint gives more space to open source softwareand the ability of students to implement themselves the tools they need.
17
4.2 Sensor network
Nowadays, monitoring of climate parameters is already done through fixed sensors. Thesesensors are able to measure climate parameters at certain moments in time and are located atknown positions; the problem is that they are stationary and as a result they cannot measureparameters everywhere. Continuous measuring of climate parameters would give the climateresearchers the ability to derive patterns about the dispersion of a parameter or combinationsof parameters, in areas of interest, as well as to explain how these patterns are related to spaceand time. Therefore, the installation of a dynamic sensor network capable of measuring severalparameters at every location in the campus area would be a good solution towards the campusclimate research. Moving platforms, easy and light to carry, travelling around the universitycampus, combined with fixed platforms located at known points, would make it possible tocapture weather or climate parameters, useful during the campus climate research.
Several problems appear however when continuous localisation is discussed. GNSS are not yetcapable of providing coverage in every place and therefore a smart way to obtain coverage evenin blinds spots of the campus or indoors is needed. When that constraint is ensured, then everyclimate parameter, able to be measured directly or indirectly,i.e. by a sensor or a sensor networkrespectively, can be tracked along the campus.
Due to the limited time available for this project, the positioning continuity will be testedoutdoors and in blind spots of the campus. When that is complete, indoors positioning willonly depend on the technology combination that will be used.
4.2.1 Sensors for climate research
A sensor for climate research could be any sensor that measures parameters related to a region’sclimate. Already in section 2.3 the parameters enabling urban climate research were presented.There, a distinction was made between the parameters affecting urban climate in dynamic andstatic ones. This section will be acquainted with the dynamic parameters, since these are theones that are measurable with fixed and moving platforms located at different places in thecampus; their data will be used complementary. The static parameters affecting climate, arerelated to objects’ properties and layout and will be treated through the 3D model.
4.2.1.1 Fixed platforms for climate research
Sensors are considered fixed when located at any stationary point, i.e. at the top of a buildingor ground-stations (see figure 4.1a). The broadcasts of fixed platforms are stored in a serverand the records are usually taken in real time. The advantage of having fixed sensors to carryout measurements, is that after some time, the observer knows the measurement pattern andthen the results can be explained; in terms of other weather or climate parameters that affectthe measurements, e.g. shade, wind, sun exposure.
4.2.1.2 Moving platforms for climate research
Moving platforms are sensors that can measure weather and climate parameters at any place,in combination with the appropriate software and a wireless connection, can give real time mea-surements. The advantage of having mobile sensors is that measurements of certain parameterscan be gathered even from places where fixed sensors are not available or there are changes inthe environment that cannot be detected by the fixed sensors. This advantage of the mobileplatforms, moves one more step towards the CCC since continuous tracking of parameters isenabled in space and time, i.e. when a person holding a mobile sensor walks along Mekelweg,
18
Figure 4.1: Fixed and mobile sensors (a. fixed weather station, b. moving temperature sensor,c. MIMAQ moving sensor box for air quality measurements)
a continuous line of measurements is taken for a long distance and even small changes can bemonitored related to the surroundings’ microclimate.
Several types of mobile sensors are available on the market. These can be sensors that makea single measurement of a parameter, e.g. temperature sensors (see Figure 4.1b), or multi-sensor platforms that are designed to provide measurements of several parameters that affect aphenomenon, e.g. MIMAQ sensors analyse the air quality by measuring temperature, humidityand gaseous pollutants concentration (see figure 4.1c).
Based on the categorization of the parameters related to urban climate in section 2.3, the sensorsthat could be used for dynamic parameters measurements are:
• Thermometers - temperature sensors: can be either fixed or mobile. They aresensitive to weather conditions and therefore have to be protected. In case of statictemperature sensors, there are certain directives following their installation in open areas.In case of dynamic temperature sensors, most of them should be covered with an insulatingmaterial, i.e, neither elevating nor decreasing temperature around the sensor, so as to bekept free from direct solar radiation or insolation and broadcast with accuracy.
• Barometers - barometric/air pressure sensors: can be either fixed or mobile butare usually parts of fixed/ground- based weather stations. They are used to measure at-mospheric pressure, i.e. pressure exerted by the atmosphere, using water, air, or mercury.Pressure tendency can forecast short term changes in the weather whereas during analysisof these measurements, surface troughs, high pressure systems, and frontal boundaries canbe defined Wikipedia (2010a). These sensors could be useful for larger scale phenomenaand therefore the effect of barometric pressure changes in the campus area is doubtful.
• Hygrometers - humidity sensors: can either fixed or mobile. They are used formeasuring relative humidity that is, the amount of water vapor in the air. Relativehumidity is an important metric used in forecasting weather. Humidity indicates thelikelihood of precipitation, dew, or fog and is closely related to temperature. High humiditymakes people feel hotter outside in the summer because it reduces the effectiveness ofsweating to cool the body by reducing the evaporation of perspiration from the skinWikipedia (2010c). Humidity, in combination with temperature, is relevant to thermalcomfort, one of the main issues in urban climate change.
• Anemometers and wind vanes- wind speed and wind direction sensors: arefound in fixed weather stations. Anemometers are devices that measure wind speed andas a result wind pressure since those two parameters are closely related. Wind vanesare instruments to show the direction of the wind and are usually placed at the highestpoint of a building. Anemometers could be useful during urban climate research since in
19
combination with wind vanes can define the wind pattern in an area. The possibilitiesfor space rearrangement and alternative wind energy use, especially in countries like theNetherlands where strong winds are frequent, can therefore be evaluated.
• Rain gauges - precipitation sensors: are found in fixed weather stations.A rain gaugeis used by meteorologists and hydrologists to gather and measure the amount of liquidprecipitation (solid precipitation is measured by a snow gauge) over a set period of time.Precipitation data would be very important during urban climate research in order tocontrol the amount of water reaching the ground and whether this amount satisfies theneeds of the urban site. Precipitation is moreover very important for vegetation andphotosynthesis.
• Disdrometers - sensors for drop size distribution: are found in fixed weather sta-tions. A disdrometer is an instrument used to measure the drop size distribution andvelocity of falling hydrometeors. Some disdrometers can distinguish between rain, grau-pel, and hail Wikipedia (2010b). Disdrometers may not be the most useful sensors forurban climate research however, their information about hydrometeors’ velocity and sizecan give an idea of the damage related to these phenomena.
• Pollution sensors: can be either fixed or mobile. An air pollutant is known as a sub-stance in the air that can cause harm to humans and the environment. Pollutants canbe in the form of solid particles, liquid droplets, or gases whereas they may be natural orman-made. Among the major pollutants are SOx, NOx, CO, CO2, volatile organic com-pounds and particulate matter. These pollutants can either stay in the urban air as gasesor cause ground contamination reaching the ground as solid particles or liquid drops. Inany form, they are proved harmful and they intensify phenomena such as the greenhouseeffect or the ozone layer hole (see Figure 4.2). Urban air quality is listed among the world’sworst pollution problems in the 2008 Blacksmith Institute World’s Worst Polluted Placesreport Wikipedia (2010d).
Figure 4.2: Causes and effects of air pollution: (1) greenhouse effect, (2) particulate contamina-tion, (3) increased UV radiation, (4) acid rain, (5) increased ozone concentration, (6) increasedlevels of nitrogen oxides Wikipedia (2010d)
• Human tracking: Nowadays, movement sensors are used in order to define the amountof people passed by an area. However, continuous sensor tracking will give the abilityto track people along with measurements, i.e. the people who carry the sensors andtherefore obtain information about the peoples’ habits throughout the day. Monitoringpeoples’ behaviour in an urban area would be very interesting in order to define which isthe relation of peoples’ activity to urban climate change.
20
4.2.2 Available sensors at the TU Delft campus
At the moment, the university has access to data from several fixed weather stations. Approx-imately every building in the campus has some weather instruments on its roof. The weatherstation located at the top of the EWI building is equipped with thermometers, barometers,hygrometers, anemometers, rain gauges, disdrometers as well as other weather instruments notof certain interest to urban climate research. Furthermore, outside the university, in the Botan-ical garden, one more fully equipped weather station is accessible. These weather stations areconnected to servers and give real-time data.
On the other hand, it is the moving platforms. Several particles are available in the universityand others can be ordered at any time based on the need. It is about cheap and light sensorsthat make it easy to carry. Their data are either saved temporarily in SD cards that are attachedon them or, when they are connected to a laptop can broadcast in real time.
4.3 Positioning systems
Positioning is a key part of this project. Through the use of positioning systems,the parametersmeasured obtain a spatial and a temporal character and that is a tool to work towards theurban climate research. Finding the position where a measurement is taken, enables connectionof the measurement to the surroundings and then the measurement has a context. Positioningis then transformed to localisation.
Several positioning methods are available today that give to the user the ability to positionhimself under different circumstances; however, not all these techniques are suitable for any sit-uation and their functionality depends a lot in the area’s specificities. In urban areas, includingthe TU Delft campus, the limitations of positioning systems become evident due to buildingdensity that can cause limited line-of-sight to the satellites, multipath and signal propagationlosses. Global Positioning Systems (GPS) are widely used around the world and considered asa backbone for navigation and positioning, however, satellite geometry and constellation is notalways strong enough to give an accurate position in urban environments.
These limitations of GPS may urge the users to use two or more positioning techniques duringthe implementation of a certain task in order to achieve in availability, accuracy and continuity.For the needs of this project, continuous positioning has to be enabled outdoors and in theblind spots of the campus. Therefore, a position technique combination that would cover theseneeds has to be investigated.
Blind spots are locations on the Earth where during several time intervals, not enough satellitesare available to get a position fix. This situation appears frequently in urban areas due tobuilding density and shape that limit the line of sight to the satellites. Blind spots in position-ing systems appear to stop the positioning continuity and therefore need to be treated whencontinuous positioning is researched.
4.3.1 Positioning techniques applicable in campus climate research
In this section, an overview of the positioning systems that can be used for positioning in thecampus area is given.
4.3.1.1 Global Positioning System (GPS)
Global Navigation Satellite Systems (GNSS) are worldwide satellite navigation and positioningsystems which work in all weather conditions, anywhere on the Earth and enable users to have
21
a precise positioning. These systems are based on range and time between the satellite andthe receiver. The Global Positioning System (GPS) is the most popular and in fact the mostreliable among the GNSS that are available today.
Availability and precision are the most important aspects in GPS. They are both dependenton the number of satellites in view and they are often influenced by weather conditions. Asfar as satellite availability is concerned, the problem is focused either on the positions of thesatellites,i.e. being above the horizon or in the low signal-to-noise ratio, i.e. the satellite signalis too weak to capture. The latter case is usually present in urban areas or in cases of indoorpositioning where multipath or signal propagation limitations, i.e. obstruction by the buildings,cause the signal received to be weak. GPS receivers are divided in GPS receivers withgeodetic application and GPS receivers with GIS application. For applications like theone we treat the second category is relevant. These receivers are C/A code GPS receivers withaccuracy of 1-5 meters, inexpensive and portable. However, in urban environments, normalGPS receivers may not be functional. High sensitivity GPS (HSGPS) extend the use of GNSSin environments where the signal is attenuated and reflected. HSGPS receivers can search forsignals very quickly to acquire the position even in areas with weak signal. Conventional GPSreceivers integrate the received GPS signal in 1ms, that is the same time as a complete C/Acode cycle whereas HSGPS receivers are able to integrate the incoming signals for up to 1000times longer than this and therefore acquire signals up to 1000 times weaker. However, stillHSGPS do not give the accuracy needed for blind spots sometimes. The signals are either highlyattenuated or suffer multipath.
4.3.1.2 Assisted GPS (A-GPS)
Assisted GPS assists a GPS receiver to overcome the problems associated with Time to First Fix(TTFF) and the low signal-to-noise ratio that are encountered under some situations which arediscussed above. It uses an assistance server to acquire needed satellite data to shorten the timeneeded to determine a location using GPS. A-GPS provides two types of assistance. Firstly,A-GPS acquires the navigation message sent by the satellites from the server and secondly,enable position fixing in environments with miserable signal-to-noise ratio by aggregating allthe signal energy contained in the received data Misra and Enge (2006). The assistance datacan be sent through any communication link, e.g. cell phone channels or Wi-Fi.
4.3.1.3 Global System for Mobile communications (GSM)
Cell towers form a system of a cellular communication network so as to allow mobile phones tocommunicate with each other. The basic positioning technique in this method is Cell-ID whichis based on the capability of the network to estimate the position of a cell phone by identifyingthe cell tower that the device is using at a specific time. The accuracy of this technique is lowbecause cell towers can support ranges up to 35 kilometres (Le, 2009). When the cell towerdensity increases, the accuracy gets better.
4.3.1.4 Inertial Navigation System (INS)
An Inertial Navigation System (INS) is a system that estimates the device’s current positionrelative to the initial position by incorporating the acceleration, velocity, direction and initialposition. An INS system typically needs an accelerometer to measure motion, a gyroscopeor similar sensing device to measure direction, and a computer to perform calculations. Thedependence of the system to the initial location as well as the position error drifts over locationmay limit the performance of the system Le (2009).
22
4.3.1.5 Wireless Fidelity (Wi-Fi)
Wireless Fidelity (Wi-Fi) is a wireless technology which follows IEEE 802.11 standard. A Wi-Fibased positioning system uses existing Wi-Fi access points installed in a facility and radio cardsalready present in the user’s device. The received signal strength (RSS) or the received signalstrength indicator (RSSI) is used to calculate the position of the tracked object Ekahau (2009).A limitation of the Wi-Fi positioning would be the signal strength variation due to multipath.
Wi-Fi positioning can be achieved through four different techniques, each one with certainadvantages and disadvantages. Among those techniques, three are based on signal strengthproperties and the other one on other properties.
Non signal strength-based Wi-Fi positioning
• Angle of arrival Given the coordinates of APs, and the angle between client/AP andNorth, triangulation can give a positioning result by calculating the location of the client.However, this method requires a directional antenna, which is expensive and not availablein mobile devices. Furthermore, the No Line Of Sight (NLOS) error is significantly largeindoors and therefore the angle measurements may be degraded. That could impact thepositioning result.
Signal strength-based Wi-Fi positioning
• Cell ID Cell ID based Wi-Fi positioning reports back the AP with the highest signalstrength as the location of the mobile client/user. The AP with the highest signal strengthis in most cases the closest AP. Cell ID positioning can be extended by the user to reportback areas where a combination of APs can be identified. This requires mapping of thecoverage area to determine which areas are able to detect certain APs. Similar to GSM,in Wi-Fi positioning the accuracy depends on the density of the APs and the size of eachAP’s cell coverage.
• Trilateration Given the coordinates of APs and the distance from APs, trilateration candetermine an accurate position. However, it is very difficult to accurately measure thedistance from the client/user to each AP.
Trilateration is a method of determining relative positions of objects using the knownlocations of two or more reference points. Based on the measured signal strength ofthese points the distance between them and the unknown point can be calculated. Inorder to determine the location of a point on a 2D plane at least 3 reference pointsare needed. Unfortunately, 3 reference points are not always available and thereforetrilateration cannot be used for positioning in the campus.
• Fingerprinting Each location has a unique set of detectable APs and associated signalstrengths. This set is known as a fingerprint. Fingerprinting involves surveying the areaof coverage, recording the Wi-Fi fingerprints and storing the data in a database. Then,finding the location of a client or user, involves measuring the current fingerprint at theunknown location and matching it to a fingerprint of the database.
Fingerprinting does not require knowledge of the APs positions, contrary to the above-mentioned techniques. Furthermore, it does not require distance estimation between theAP and the client, and therefore static objects in the environment, affecting usually signalstrength, do not affect this system. However, an object’s removal or a significant structuralmodification on a building, that will affect the signal strength in certain locations, maydegrade the results. Then, the coverage area must be re-surveyed and the database shouldbe updated.
23
Fingerprinting includes two phases: training/surveying and positioning. A fingerprintingdatabase is created in the training phase. In positioning phase, matching of the finger-prints to the database records is done either with deterministic or probabilistic methods.In deterministic methods the fingerprint are directly matched to the database’s fingerprintswhereas in probabilistic methods the probability of the match is returned together with anerror of the estimation. That makes the probabilistic methods often preferable over the de-terministic methods. Deterministic methods include ’Nearest Neighbor’ (NN) algorithm,Manhattan and Euclidean distance algorithm whereas probabilistic approaches includek-NN, k-weighted NN, smallest polygon and Gaussian processes (to model the probabilityP(z | x) where z=measurement (signal strength from visible AP) and x=location) Ferris(2006).
Wi-Fi fingerprinting would be a good solution for the complementary positioning systemof GPS. The university already owns a densely populated network of APs and therefore,a software for fingerprinting and the area survey are needed.
4.3.1.6 Bluetooth
Bluetooth is a wireless communication method used by two devices over short distances; it isthe IEEE 802.15 standard and is similar to Wi-Fi. Bluetooth is similar to Wi-Fi communicationbut with limited reange and communication speed (10m for Class 2 over 3Mb/sec) Le (2009).Bluetooth uses frequency hopping that enables it to work with very low signal-to-noise ratio.A bluetooth positioning system performs positioning using the received signal strength of thesurrounding bluetooth access points. It is not affected by non Line of Sight (NLOS) however itis strongly affected by noise signals.
4.3.1.7 InfraRed
In infrared (IR) positioning, each tracked object carries an emitter that periodically transmitsand IR beacon containing a unique code. IR receivers are placed throughout the facility anddetect the beacons transmitted by the object’s emitter. The position of the object is thencalculated based on the distance to the IR receivers Ekahau (2009). IR positioning providesmoderate to high accuracy however it is proved sensitive to sunlight, multipath effects andNLOS.
4.3.1.8 Ultra-WideBand (UWB)
Ultra-Wideband (UWB) positioning systems use scanners installed throughout the facility, thatcontinuously monitor UWB radio transceivers attached to clients. UWB sytems, however, op-erate using radio signals having very wide bandwidth, and position calculations are made basedon time-of-arrival techniques instead of signal strength, which leads to fairly good positioningEkahau (2009).
4.3.1.9 Radio-Frequency IDentification (RFID)
An active Radio- Frequency Identification (RFID) positioning system includes a network ofRFID scanners installed throughout a facility that search for either active, i.e. radio transceiversor passive tags that are attached to objects. The system can determine the object’s position
24
within the RFID scanner’s range. This system is proved not practical for real-time trackingand suffers from interference with Wi-Fi Ekahau (2009).
4.3.1.10 Ultrasonic system
The Ultrasonic system uses ultrasound signals from beacons to estimate the position of a mobiledevice with trilateration. The system comprises of ultrasonic transmitters and receivers. Thesystem’s functionality is strongly affected by multipath propagation and NLOS giving howevermoderate to high accuracy in ideal conditions Ekahau (2009).
25
4.3.2 Technique trade-off
A combination of position techniques has to be chosen in order to obtain continuous trackingof position outdoors and in the blind spots of the campus. The performance of each of thecombinations that will be presented greatly depends on the specificities of the application area;however, in the TU Delft campus, the scenery is relatively common to an urban environment,with tall buildings blocking the line-of-sight to the satellites and creating multipath condi-tions.Multipath and signal propagation losses are actually the most important characteristicsthat have to be taken into account when searching for the appropriate combination, since themethods chosen have to make up for each other’s limitations in a densely built environment.
GPS technology has to be used anyway since it is proven functional outdoors, in open spaces withhigh accuracy values. Furthermore, HSGPS are even more efficient using the same technologyhowever using receivers that capture quicker the weak signals in urban environments. As aresult, since the first part of the combination is defined, the research is carried around thesecond contributor that will cover the GPS weaknesses.
Based on the properties of each positioning method as described in section 4.3.1 a table wascreated illustrating the strengths and weaknesses of several combinations, i.e. HSGPS and acontributing technology, in infrastructure, coverage and accuracy for positioning in the campusarea. These three properties of the positioning systems are moreover the main concerns whenperforming research over the compatibility of a positioning method for a specific task. Moredetailed information for continuous positioning combinations can be found in Appendix A.1.
Based on table 4.1 one can define which is the technique combination that is more efficientto enable continuous positioning in an urban environment like the campus environment. Itshould be mentioned however, that none of this combinations can promise successful continuouspositioning with guaranteed results in the campus environment since the building configurationand the properties of the building materials can create unexpected conditions and blind spotsfor many of these combinations.
In order to choose however the positioning technique that is more beneficial for continuouspositioning in the campus, it was decided that some load has to be put in the requirementsposed for this product at the start of this project. Therefore, the limitation of using the existinginfrastructure during the implementation phase was a leading factor. Positioning techniquesthat use the existing hardware could be the combinations of the combinations of GPS/ GSMand GPS/Wi-Fi.
After the first filtering, these two combinations were investigated in more detail. The com-bination of GPS and GSM uses cell towers and mobile phones to perform positioning. Thistechnique, cannot work properly however in urban environments, since the accuracy providedis often less than the 30-50 meters that is needed for climate research applications. Therefore,only one combination is left, that is GPS in combination with Wi-Fi positioning. The universitycampus offers a very good Wi-Fi network able to cover areas that are close to buildings andconfront problems with the GPS positioning. As far as the availability and the accuracy areconcerned, these are much dependent on the APs density and their configuration. The networksefficiency for such applications will be investigated through several applications.
Therefore, there is a winner. Wi-Fi positioning will contribute to GPS positioning to achievecontinuous position tracking in the campus. The final step, would be to choose between theWi-Fi positioning methods presented in section 4.3.1.5. The leading principle for such a choiceis the manufacturer’s claimed accuracy for each method. Analysis of the information given,show that the most robust solution is Wi-Fi fingerprinting. It is about a method that does notrely on distance calculations, and therefore multipath does not affect the system’s functionalitywhereas the use of unique id’s for each location makes it stable enough. In this final choice,software availability for Wi-Fi fingerprinting, courtesy of the company Lumiad (Lumiad, 2010),made the choice even more clear.
26
Positioningcombina-tion
Infrastructure Coverage Accuracy
GPS andGSM
Cell towers and mo-bile phones
In urban areas cannotaccount for GPS limita-tions
In urban areas accuracy de-pends on the density of the celltowers and the distance to thedevices/clients.
GPS andINS
Accelerometers, gy-roscopes, powerfulcomputers for theprocessing
Can cover some of theGPS blind spots
Dependency of accuracy on ini-tial location and on position er-ror drifts
GPS andWi-Fi
Wi-Fi access pointsand Wi-Fi enableddevices
Can cover some GPSblind spots with a goodnetwork
Accuracy differs between differ-ent methods and depends muchon signal strength variations. Ingeneral an acuracy of 5-10 me-ters is given
GPS andBluetooth
Bluetooth accesspoints and Bluetoothenabled devices
Can cover some GPSblind spots with a goodnetwork, well populatedsince Bluetooth range islimited
Accuracy depends on the rangeof Bluetooth and the noise fromexternal sources
GPS andIR
IR transmitters andreceivers
Can cover some GPSblind spots with a goodnetwork
Gives good accuracy when noobstacles come in between butin the opposite case suffers frommultipath and NLOS
GPS andUWB
Need for transmit-ters and receivers at-tached to clients
GPS blind spots can becovered in certain areaswith a very good trans-mitter network and re-ceivers at certain dis-tances
The accuracy depends on time-of- arrival and is therefore af-fected by multipath
GPS andRFID
Need for transmit-ters and receivers at-tached to clients
GPS blind spots can becovered in certain areaswith a very good trans-mitter network and re-ceivers at certain dis-tances
The accuracy is much depen-dent on signal strength and thesystem suffers a lot from inter-ference with Wi-Fi making itnot suitable for positioning inthe campus
GPS andUltra-sonic
Need for transmit-ters and receivers at-tached to clients
GPS blind spots can becovered in certain areaswith a very good trans-mitter network and re-ceivers at certain dis-tances
The accuracy is much affectedby multipath and NLOS makingit not suitable for positioning inurban environments
Table 4.1: Identification of strengths and weaknesses of several positioning techniques combi-nations
27
4.3.3 Limitations of the chosen positioning combination
Wi-Fi fingerprinting and GPS combination appears beneficial as a method for continuous po-sitioning with its main advantage being the already existing infrastructure, i.e. Wi-Fi accesspoints densely populating the campus and the accuracy given for climate parameters investi-gation. Trough this combination, the Wi-Fi network will try to make up the weaknesses ofHSGPS and vice versa. The limitations and the advantages of both the positioning methodsare however very important and will be discussed further in this paragraph. This analysis willgive a better understanding on the reasons why this combination was chosen whereas it willhighlight the steps that need furhter investigation for a completely functional system.
4.3.3.1 GPS blind spots in the campus
GPS blind spots appear frequently in urban areas due to building density and shape that limitthe line of sight to the satellites. In urban areas and under mild conditions, 95% of GPSavailability can be reached with street widths of more than 20m and building heights of notmore than 20m Kleijer et al. (2009). This means that, under these urban conditions, for asingle position, at least four satellites will be in view for 95 % of time during one daytime span.However, this is not always the situation in urban areas and specifically in TU Delft campus.Building heights can be greater than 20m, e.g. EWI building, Aerospace building and streetwidth in some locations, e.g. between buildings may get much less than 20m. As a result, thesecases have to be investigated as blind spots that can constrain the continuity in positioninginside the campus. Even with HSGPS the situation is not much better. Multipath and signalpropagation losses are evident there too however less intense.
A research around GPS blind spots can start for a regional skyplot. Skyplots illustrate thesatellite trajectories, with respect to elevation and azimuth, over a given ground site and provideintuitive feel for satellite geometry which would reveal the impact of obstructions on satellitevisibility Marshall (2002).
Considering the skyplot of Delft, see Figure 4.3, there is no satellite distribution in northazimuth direction; thus an obstacle’s or building’s location is very important. When a buildingis located south of the receiver, this building will block the line of sight to the satellites in thesouth direction and then availability and/or accuracy problems are evident. The hole formed bythe lack of satellites in the north causes lower visibility and availability in north-south streetsthan in east-west streets Kleijer et al. (2009). Due to the satellite imbalance in the north-southdirection, the Dillution Of Precision (DOP) values and therefore accuracy, will be relativelypoor, even if the number of satellites is enough to get fixed position.
The satellite availability in an area and therefore the areas that need support by other posi-tioning systems, rather than GPS, can be defined by GPS blind spot maps. These maps can becreated based on calculations using the line-of-sight of a point to the satellites and the satelliteconstellation in a region. The number of satellites visible in a known position can be computedwhen the elevation and azimuth of all GPS satellites are known, as a function of time. Whenthe satellite ephemerides is known, a subset of this data i.e. an array of [time, PRN, elevation,azimuth] can be used for these computations.
In fact, one has to realize that visibility of the satellites is not only determined by the locationof the observer and obstructions around him, but also by the moment of observation as thesatellites are in orbit Verbree and Zlatanova (2007). Therefore, depending on the local visibility,an elevation mask will be set as a function of azimuth to filter out the invisible satellites. Thedefault elevation cut-off angle is zero which is equal to horizon. The algorithm for the creation ofa map of GPS availability can be found in the Appendix A.2. Figures 4.4, 4.5 illustrate severalsatellite availability maps. Through these maps, the need for a complementary positioningsystems in places with low availability is evident.
28
Figure 4.3: Delft Skyplot, with 10 degree cut-off angle over a 24 hour period, from the GNSSToolbox. Although the skyplot is made in the year 2000, the distribution is still the same.
Figure 4.4: Calculating satellite availability in OTB building, using the line-of-site from a pointto the satellites Verbree and Zlatanova (2007)
29
Figure 4.5: Satellite availability around OTB building Verbree and Zlatanova (2007)
Unfortunately, due to lack of time, blind spot maps were not created and the blind spots ofthe experimental area were defined after a normal survey. This is a highly demanding task andcould only be applied in this case, where the experimental area had a limited width. Muchmore efficient results would have been obtained from a blind spot map.
4.3.3.2 Wi-Fi blind spots in the campus
However, it is not only GPS that present blind spots. Wi-Fi network is either not so denselypopulated in some areas of the buildings in order to be used as a positioning method or obstruc-tions cause very low signal values. A survey of the OTB building was made in order to definethese weak signal areas. The survey was performed with Ekahau Site Survey (ESS), Lumiad(2010). Figures 4.6, 4.7, 4.8 illustrate the results of the first measurements in the OTB buildingto test the accuracy, coverage and quality of the Wi-Fi network for positioning.
These maps, same as the GPS blind spots maps could be very useful as a way to define theareas where the Wi-Fi infrastructure needs re-enforcement with access points. Furthermore, amap that would give the blind spots of both GPS and Wi-Fi network in the experimental areawould be very useful for research around a more robust system to account for the blind spotsof both techniques.
4.3.3.3 Wi-Fi fingerprinting in the campus
Some other constraints of the system is that the Wi-Fi network can change much over time.This changes refer to replacing APs, changing channels of the APs, changing signal strengthor placing new APs. When a change is done within the network, the affected area needs to besurveyed again. Because surveying takes quite a lot of time, one would want to have a networkas stable as possible when using Wi-Fi for positioning purposes Lumiad (2010).
30
Figure 4.6: First measurements in the ground floor of the OTB building indicating the accuracyof the Wi-Fi for positioning. ESS was used for the fingerprinting.
Address Name Faculty/Building Number AP
Jaffalaan 9 OTB Research Institute + Education& Student Affairs
OTB 25
Mekelweg 5 Auditorium AULA 20
Table 4.2: Number of access points in the experimental area
4.3.4 Implementation of continuous positioning in the TU Delft campus
The limitations of both the positioning techniques were taken into account during the implemen-tation process and therefore the results were somehow expected given the existing infrastructureand its limitations. The performance of the system combining HSGPS and Wi-Fi fingerprintingfor continuous position tracking was tested in the area covering the OTB and Aula buildings ofthe TU Delft campus. This would be the experimental area.
HSGPS positioning are considered functional for most of outdoors whereas for the blind spots,Wi-Fi fingerprinting was used. The APs available in the experimental area that could be usedfor positioning are shown in table 4.2.
Hardware used for the measurements was a U-blox AEK-4t receiver, courtesy of the Mathemati-cal Geodesy and Positioning (MGP) group of the Faculty of Aerospace Engineering and a tabletPC used for Wi-Fi positioning, courtesy of Lumiad (2010). As for the software, the U-bloxreceiver uses its own software to capture and record the measurements and the tablet PC is en-abled with the Ekahau software for fingerprinting. However, since each device works separately,new software was implemented in Python in order to combine the output of the devices andfinally to keep only one record for each position, based on the accuracy given. Finally, based onthe product’s top level requirements as defined in section 4.1 and since each device works in itsown coordinate reference system, software for the coordinate transformations into the RD Newcoordinate system was also implemented. Floors plans of the OTB and Aula buildings werealso used during the implementation phase as input for the survey with the tablet PC. A more
31
Figure 4.7: First measurements in the ground floor of the OTB building indicating the coverageof the Wi-Fi network during fingerprinting with the ESS.
detailed description of the results of each step is given in the following paragraphs, whereas thecombination algorithm that enables continuous positioning is also discussed.
4.3.4.1 U-blox HSGPS receiver functionality
The output of the U-blox HSGPS receiver is in standard NMEA (National Marine ElectronicsAssociation) 0183 format. The message, in NMEA format, is extracted from the port in whichthe receiver is connected and is then filtered using software written in Pyhton. During thefirst filtering, only the GPGGA part of the message is kept. This part contains the fix data,providing the 3D location and accuracy data. Afterwards, a second filter is applied in theGPGGA sentence and then the remaining information are time, latitude, longitude, fix quality,number of satellites and accuracy (HDOP) value. These data are enough to obtain positionand define the accuracy of the measurement. The output of the HSGPS receiver after the firstfiltering is presented below.
$GPGGA,140238.00,5200.18785,N,00422.26513,E,1,04,3.49,0.1,M,47.1,M,,*5A
• Sentence Identifier (GPGGA)
• Time (140238, i.e. fix taken at 14:02:38 UTC)
• Latitude (5200.18785 N)
• Longitude (00422.26513 E)
• GPS Fix (1)
• Number of Satellites (04)
• Horizontal Dilution of Precision (HDOP) (3.49)
• Altitude (0.1 M i.e. meters, above mean sea level)
32
Figure 4.8: First measurements in the ground floor of the OTB building indicating the qualityof the Wi-Fi network signal during fingerprinting with the ESS.
• Difference between WGS84 ellipsoid and global mean sea level (47.1 M)
• Age of DGPS data
• DGPS reference station id
• Checksum (5A)
4.3.4.2 Wi-Fi fingerprinting functionality
The Ekahau software is used for Wi-Fi fingerprinting. Wi-Fi fingerprinting works in two steps asdescribed in section 4.3.1.5. Surveying is done with the Ekahau Site Survey (ESS) software. TheESS is a simple to use but powerful software tool for Wi-Fi network planning and verification,which gives to the surveyor a plan of the system’s coverage and performance. ESS creates,processes and stores the fingerprints of surveyed locations into a database in the server. Floorplans of the surveyed area are used so as the user to match the position of the fingerprints inspace.
Positioning goes next. At this step, matching of the positions to the fingerprints in the databasetakes place. The Ekahau Positioning Engine (EPE) is used here. It includes several patentedalgorithms and methods to calculate locations in real-time within the Wi-Fi network and uses thedatabase records to locate the client based on the fingerprints of the Surveying step . Therefore,the client’s Wi-Fi device sends the Wi-Fi signals received to the server and through the EPEthe received signals are related to the fingerprint saved in the database. After processing, thedatabase records the position.
WiFi fingerprinting uses the Received Signal Strength (RSS) or the Received Signal StrengthIndicator (RSSI) of the APs in view to calculate a fingerprint to calculate a unique location.Ekahau software has high requirements on the WiFi network. The positioning client has tohave in view, two APs with an RSS of at least -65dBm or three with at least an RSS of -75dBmas a minimum for an accurate position estimate. Accurate here means 3-5 meters of accuracy
33
(based on Ekahau RTLS specifications). The system works better if the APs are spread ina zigzag form and not in one straight line. When APs are placed in an asymmetric (zigzag)pattern, the difference between the fingerprints is larger because there is more variation in theAP and RSS combinations. Then the position calculation gets a more accurate result. WiFinetwork cards, for example installed in a laptop or other mobile device, have different methodsof calculating the RSS/RSSI of a received signal. These different calculation methods result indifferent signal strengths. The average error of a WiFi card is from -3dBm to +3dBm creatinga measurement error of 6dBm for the position estimation. All these requirements are describedin Ekahau (2009).
The Wi-Fi network of the TU Delft is planned based on data coverage. The result of this isthat most Access Points are placed in line. Also because of the ease of maintenance the AccessPoints are set to Dynamic Channel Allocation (DCA), when the channel on which the AccessPoint is transmitting is interfered the it will automatically change to another, less interfered,channel. When this happens the fingerprints collected by doing the survey have became useless.The same goes for Transmit Power Control (TPC), this technique automatically reduces itstransmit power when other Access Points are in range. Both techniques are applied in theWi-Fi network of the TU Delft.
4.3.4.3 Coordinate transformations
The coordinate transformations is one of the top level requirements for the tracking module.The purpose of setting up this part is to unify the coordinate systems in Wi-Fi and GPS so as toenable the comparison of their accuracy based on one system and also to make the coordinatesin the output of the sensing part consistent to those in the 3D model.
The transformation is defined and programmed in Python, and it includes two parts: one isto convert the Ekahau coordinate system coming during Wi-Fi positioning to RD coordinatesystem and the other is to transform WGS-84 in GPS to RD coordinate system.
Ekahau Coordinate System to RD Coordinate System The Ekahau software packageis adopted to perform Wi-Fi positioning, in which the coordinate system is actually a pixelcoordinate system with the origin at the top left of a picture. This picture is the floorplan ofthe building. The pixel coordinate system of a sketchy OTB floorplan is shown in the Figure4.9. The four points (1,2,3,p) of OTB are used for the transformation; they all have knownRD coordinates and pixel coordinates. The three points (1,2,3) are used in the transformationwhereas point p is used to test whether the transformation works. The coordinates of the 4selected points are presented in Table 4.3.
Figure 4.9: Pixel coordinate system for OTB floorplan
34
Pixel Coordinate System RD Coordinate System
1 (u1, v1): (943,1237) (x1, y1): (85195.228,446500.739)
2 (u2, v2): (664,1232) (x2, y2): (85188.394,446517.509)
3 (u3, v3): (656, 787) (x3, y3): (85216.134,446529.016)
p (up, vp): (946, 782) (xp, yp): To be calculated
Table 4.3: Coordinate transformations from local to national coordinate system.
WGS-84 to RD coordinate system WGS-84 is the reference coordinate system used by theGlobal Positioning System. The GPS receiver exports points of which positions are representedby latitude and longitude. An approximation approach of the transformation from WGS-84to RD is applied here which references the formulas and coefficients in Schreutelkamp and vanHees (2001).
4.3.4.4 Combination Algorithm
Positioning is already enabled with the positioning methods separately. Furthermore, throughcoordinate transformations, the results of both methods are in the regional coordinate system.Then, the combination of positioning systems has to be enabled so as to obtain one singleresult for each position. The algorithm combining the HSGPS and the Wi-Fi signals to obtaincontinuous position tracking will be presented in this paragraph, whereas the Pyhton code isgiven in the Appendix A.3.
Figure 4.10 illustrates the GPS and Wi-Fi fingerprinting combination algorithm. The mainidea is that both HSGPS and Wi-Fi enabled devices receive at the same time signals for thesame location. What matters then, is how accurate is the position estimate. To define that, athreshold has been put in accuracy. The device that will capture first an accurate position, i.e.below this threshold, will be used for positioning at that certain location.
Therefore, the accuracy of one system over the other is important to get the final position. Thisaccuracy comes for both GPS and Wi-Fi in two steps, the quality of the position estimate andthe receivers accuracy of the positioning system. For GPS the HDOP value is used where forWi-Fi the quality value acquired from Ekahau is used (TAG. posquality). This value is thenmultiplied with the receivers accuracy, which for the U-blox GPS is 6 meters as shown in 4.3.4.1and for the Wi-Fi 33.6 meters as shown in 4.17.
In GPS receiver, the GGA message is checked for a result ,i.e. the estimate accuracy value(GPS Fix). Depending on satellite availability and the ability of the GPS receiver to calculatethe position with the given geometry and constellation of satellites, the GPS fix value and canbe 0, 1 or 2 meaning invalid GPS result, valid GPS result or DGPS result respectively. Thenthere are three cases:
• When the GPS Fix is valid, then the estimate accuracy (HDOP) is multiplied with thedevice’s accuracy and the result, that is the final positioning accuracy, is compared toposition quality (TAG.posquality) of Wi-Fi. The one that is closer to the threshold valueposed is the one that gives the position at that point.
• When the GPS Fix is invalid, the Wi-Fi result is checked for positioning and if that isalso invalid then the procedure starts again.
• When the GPS Fix is a DGPS result then the procedure goes as in the first case usingthe devices accuracy as defined for DGPS cases.
The coordinate transformations that will convert the receivers’ system into the regional coor-dinate system take place before the calculation of the final positioning accuracy.
35
Figure 4.10: GPS,Wi-Fi combination algorithm
4.4 Integration of tracking and sensing
The research around the effect of dynamic parameters in the campus climate, starts by moni-toring these parameters in the campus area. Until now, continuous positioning outdoors and inthe blind spots of the campus has been enabled as presented in section 4.3.4.4. Therefore, thenext milestone would be the installation of a sensor network, able to be tracked in the campus.
4.4.1 Measurement methodology
The experiment takes place around the OTB building in the campus. The objective of thisexperiment is to achieve continuous tracking of temperature using Wi-Fi, GPS and a mobiletemperature sensor. That way, position aware sensing will be enabled. Hardware and softwareused as well as the measurement area and methodology will be discussed in this section.
4.4.1.1 Hardware used in the experiment
Four hardware parts were used during the measurements. These were:
• A GPS receiver, U-blox AEK-4t
• A tablet PC, Acer C300
36
• A mobile temperature platform, TMP36GT9Z on an Arduino board
• A server
The GPS receiver is a U-blox HSGPS receiver. It uses the ANTARIS 4 Position Engine whichsupports up to 16 channel measurements. It can be used as A-GPS or for autonomous GPSoperations and has 4 Hz position update rate. As communication links, the receiver uses a USBport-outside- and a UART port -inside.
The tablet PC used is an Acer C300. It has a relatively low capabilities in terms of processingand memory however it has a very strong Wi-Fi card and a Wireless LAN adapter that allow itto be used for Wi-Fi fingerprinting. Furthermore, it has a touch screen screen enabling definitionof control points during fingerprinting. The technical characteristics of the tablet PC are givenin the Appendix A.4 .
The temperature measurements are held with an Arduino Duemilanove (see Figure 4.4.1.1)and a low voltage temperature sensor-TMP36GT9Z (see Figure 4.12). An Arduino is an open-source electronics prototyping platform based on flexible hardware and software. It can sensethe environment by receiving input from a variety of sensors and can affect surroundings bycontrolling lights, motors and other actuators Arduino.
An Arduino Duemilanove has 14 digital input/output pins (of which 6 can be used as PWMoutputs), 6 analog inputs, a 16 MHz crystal oscillator, a USB connection, a power jack, an ICSPheader, and a reset button. It contains everything needed to support the microcontroller andsimply needs a connection to a computer or a power adapter to get started. The temperaturesensor is mounted on the Arduino and then they are together wrapped in a sensor box. Thesensor box, see Figure 4.13 is connected to a computer through usb cable and the appropriatesoftware is used in order to get and log the measurements in a file.The specifications of thetemperature sensors and the Arduino board can be found in the Appendix A.4.
Figure 4.11: Arduino Duemilanove I/O board Arduino
Finally, an HP Compaq dc7800 Convertible Minitower server from TU Delft is used for al theprocessing and the storage of the data when performing the measurements. This server has verygood processing and storage capabilities whereas it runs Widows software XP Professional. Itwas proven much valuable during the measurements as well as during the positioning imple-mentation since the tablet PC and the other normal computers used had not enough processingcapabilities. The server’s technical characteristics are presented in the Appendix A.4.
4.4.1.2 Software used in the experiment
Software implemented in Python was used during the experiment in order to collect and processthe data from the three devices and obtain a single result. Same software as in the positioning
37
Figure 4.12: Low voltage analogue temperature sensor TMP36GT9Z boards (2010a)
Figure 4.13: Arduino and temperature sensor in a sensor box boards (2010b)
combination was used here, however the temperature sensor measurements had to be integratedtoo and therefore the Pyhton software had to be extended. Several Python libraries were usedthen, based on previous research and applications with the same devices.
The Arduino microcontroller has its own software and is programmed using the Arduino pro-gramming language -based on Wiring- and the Arduino development environment-based onProcessing Arduino. The temperature sensor is connected to one of the Arduino pins. Theserial connection starts and the result of the temperature measurements are shown in the mon-itor. What is interesting with this mobile platform is that the board reads the voltage from thetemperature sensor it transforms it to voltage -depending on the characteristics of the board, inour case 5V operating voltage- and then through this transformation, the voltage is used to cal-culate temperature again. So, Arduino is more of a translator that can be used in combinationwith different sensors to give different parameter measurements.
In order to skip the Arduino software and have everything in a common platform, in Python,the new software implemented uses Python libraries, e.g. pyserial, that enable reading fromthe serial port where the mobile platform is connected. Therefore, the records are taken in thePython environment and are latter logged in simple .txt format.
The Pyhton code added to the main positioning software, to acquire an integrated result withposition, measurement, time and accuracy can be seen in the Appendix A.5.
38
4.4.1.3 Measurements
Firstly the fingerprints of the experimental area were gathered using the ESS software in orderto create a database with fingerprint of the experimental area. Then, the survey of the areastarts in order to track the temperature measurements in the area around the OTB building.The steps followed during the survey are given roughly below.
• The Wi-Fi client obtains the signal strength and the MAC address of any nearby accesspoint. The information is sent through the communication link to the server and theserver calculates the clients position based on the fingerprint. The data are then sent tothe the computer that will process the data.
• The GPS receiver obtains the position details and propels the data in the same communi-cation link as the Wi-Fi data. Since Wi-Fi positioning data and GPS positioning data arein the same environment, the python script compares the GPS and Wi-Fi position withthe use of the algorithm explained in 4.3.4.4.
• When the positioning method is decided the temperature measurement is obtained fromthe sensor. The message with position and temperature is created, saved and extractedto the preferable format, i.e. .txt format, .xml format, e.t.c.
The route followed for the final measurements was around the OTB building. Measurementswere also taken inside the OTB building, passing from the one side to the other. The buildingtraversal was done on purpose to test the transition from Wi-Fi to GPS positioning when theGPS signal is completely lost.
4.4.2 Results
The results of the integration of positioning technology together with sensor technology, showthat continuous positioning of measurements in an urban environment is possible. Measure-ments can be acquired from different sensors that can be connected to the Arduino board.The results coming from the software written in Pyhton are exported to Comma Separated fileformat which then is imported into the database and can be visualised in the 3D model. Thecontent of the output is as follows:
X-coordinate,Y-coordinate,Height,Accuracy of position,Sensor value,Date Time
The X and Y coordinates are both in the “RD New” coordinate system as explained in 4.3.4.3.The value for the accuracy of the position is the same as used in the combination algorithm.The accuracy output can be later on used as input for the 3D-model in order to visualize theposition as an area and not as a point. This would be done by using the accuracy as the radiusof a circle.
The following images illustrate the database filled with the records of the survey (see Figure4.14), the resulting model in 2D (see Figure 4.15) and the same 2D model with the temperaturesand their elevation attribute (see Figure 4.16) . The resulting model in 3D can be visualisedin Figure 5.13 available in the 3D model’s results. The dots represent the positions for whichtemperature measurements were recorded.
The accuracy of the Wi-Fi positioning is shown in Figure 4.17. This accuracy is calculatedfrom a test survey done after Surveying. This test survey is used in order to match the receivedWi-Fi fingerprints acquired in each survey with the Wi-Fi fingerprints saved in the database.The ESS does the calculation and then outputs the average and 90% error of the Surveying .
39
Figure 4.14: Positioned temperature measurements stored in the 3D model’s database
Figure 4.15: Temperature measurements visualised on a 2D model of the OTB building
40
Figure 4.16: Quality of the measurements together with their elevation attribute
Figure 4.17: Wi-Fi accuracy statistics around the OTB building
41
4.4.2.1 Limitations of the system
Synchronization problems exist when the GPS, Wi-Fi and sensor values are combined to en-able continuous measurement tracking. The Wi-Fi fingerprint that is collected by the EkahauPositioning Client has to be send to the EPE, has to be matched and the position has to bewritten. The position is written in an XML stream which is polled (checked every second) fornew information. This takes some time, about 2-3 seconds depending on the connection speedto and from the positioning server. This makes the software to lose synchronisation with theGPS and sensor data. The GPS and sensor data is polled every second, and therefore this datais more recent than the Wi-Fi position. This could be solved by having some kind of synchro-nised signal coming from the WiFi Positioning Client. This software is created by Ekahau andtherefore cannot be altered. The time difference seen in the experiments done is a maximum of5 seconds but the average of the time difference is about 2 seconds.
The Wi-Fi network available in the TU Delft campus is not quite usable for positioning outdoorsbecause the signals of Access Points are not strong enough to be received outside of buildings.This, results in low positioning accuracy outdoors, using the existing Wi-Fi network. A randomset of 320 points taken within 10 meters of a building which gives an impression of the GPSavailability (figure 4.18). Using such maps one can plan better the Wi-Fi coverage in these areasto improve positioning capabilities with Wi-Fi.
Figure 4.18: Satellite availability at 320 random locations in TU Delft campus (Verbree andZlatanova, 2007)
The Position Client collects a Wi-Fi fingerprint which has to be send to the EPE, as describedin 4.3.4.2. When a fingerprint is collected and a client is not associated and authenticated withthe network, the fingerprint gets lost. This could be solved using another communication linkto send the data. When this is done the location fix and fix time is a bit extended becausethe process of association and authentication takes time and when this is skipped less collectedWi-Fi fingerprints get lost.
42
4.5 Conclusions
During this project several steps were followed towards enabling a method for continuous sensortracking. Two different positioning techniques were combined and applied to enable continuoustracking of a sensor in a sensor network. GPS and Wi-Fi positioning techniques were integratedinto one positioning system.This was done, using an algorithm to choose, each time, betweenthem the most appropriate positioning method, based on its accuracy.
It was proven in previous sections that continuous tracking using those two positioning tech-niques is feasible. Through appropriate processing of the input and with the use of the appro-priate algorithms the positioning techniques were combined in an efficient level.
Several limitations were however evident in both the positioning techniques. The limitationsdiscussed in section 4.3.3 were all proved important and constrained enough the capabilities ofthe resulting product. However, most of the limitations refer to the Wi-Fi network and theWi-Fi as a positioning method. It was proven that the Wi-Fi coverage is not adequate enoughfor this application. The APs coverage is not enough for positioning at a distance larger than 5meters from the buildings. Furthermore, the current Wi-Fi APs constellation is not appropriateto provide position with good accuracy since the signal strength is not enough. The APs arein an aligned constellation when a zigzag would be preferred to give less errors during positioncalculations.
For the Wi-Fi fingerprinting itself, several limitations are also present. Firstly a survey ofthe campus has to be realised in order to use fingerprinting for such a large scale positioning.However, a power outage would destroy everything since the channel allocation and the transmitpower are reset and then the fingerprints of the previous survy have no meaning.
During the implementation stage, it was proven difficult to apply Wi-Fi positioning even closeto the buildings, since the network coverage was quite low. Wi-Fi blind spots appeared inseveral points of the buildings’ faces. If these spots were combined with GPS blind spots, thatwould result in no position estimation at several positions. However, blind spot allocation inthe surrounding area of OTB was done, though manually. The result was quite satisfactory foronly a limited area but of course prior knowledge of the GPS and Wi-Fi coverage in the area,would be valuable to know the positions where the Wi-Fi network coverage can be improved.The time for that project was not sufficient enough to optimize the Wi-Fi coverage on thisspots.
Coordinate transformations were applied with success. Ekahau uses a local coordinate systembased on the image used as background, i.e. floor plan. The GPS receiver on the other hand,sends coordinates in WGS84. Then, both coordinate systems were transformed in the regionalcoordinate system, the Dutch Rijksdriehoek New (RD new, EPSG 28992).
Synchronization problems are also important and unfortunately during this project were notsolved. Each positioning technique spends some time to calculate the position or make thematching to the database and then the polling and data acquisition spends more time. Thatmakes the system asynchronous.
Finally, it should be mentioned that the use of Python as a programming language , producedan excellent result during the whole process. Creating Python scripts was fast and efficient whiledebugging was not hard. Furthermore, the team was able to combine all the parts i.e. GPSdata, Wi-Fi data, coordinate transformations, sensor data, e.t.c., into one script to produce asingle and robust result.
43
4.6 Future work
Availability and accuracy are the main issues related to positioning. Between them availability ismore important since when availability is succeeded then accuracy depends more on the device’sproperties. Based on this prioritization, the first aim in future work should be to optimize theset up of the Wi-Fi network. More access points should be installed in each floor, in a try toachieve better coverage and elimination of Wi-Fi blind spots. Access points distribution shouldalso be better. Their constellation should be zigzag, in order to cover as much area as possibleand to increase the fingerprinting robustness.
The way to define the areas where the Wi-Fi network needs reinforcement is by creating Wi-Fiblind spot maps based on the existing network. Such a map is illustrated in section 4.3.3 inFigure 4.7. Furthermore, GPS availability estimation maps could be used as leaders for sucha move. Figures 4.18, 4.4, 4.5 from previous sections illustrate GPS blind spots maps thatwould be nice to have during the design of the complementary particles of the Wi-Fi network.Supplementary APs could also be located outdoors to cover the needs of the network in placesfar from the buildings.
Unfortunately, multipath and signal propagation losses cannot be eliminated in an urban envi-ronment however their effect can be decreased with more powerful hardware. Another methodto consider for the reinforcement of the Wi-Fi network is the use of new network standards(IEEE 802.15.4a standard), which are proven to support higher data rates, extended range andimproved robustness against interference IEE (2010). At the same time a different Wi-Fi posi-tioning software should be used, other than Ekahau software to research around the capabilitiesof the network. Researching, creating and applying a new software, not commercial and with norestrictions should be done in order to have total control of the process. This software shouldbe server-based and the calculations would therefore be done in the server. This will solve theproblem of low hardware abilities as well as most of the synchronization problems between thedifferent devices, i.e. Wi-Fi, GPS, sensors.
The extension of positioning indoors should be one of the following steps also. Creating a betterWi-Fi network covering the blind spots of GPS and of the Wi-Fi network itself would createthe ideal conditions for indoor positioning. In such a case, other complementary positioningsystems, such as IMES or pseudolites, could be used. More information on these systems canbe found in the Appendix A.6.
Furthermore, real-time measurement tracking should be in the future plans. That would beenabled when the database would be able to accept real-time data and that is one of thefunctionalities that IBM will enable.
Finally, the limited time for the project completion and the limited resources didn’t allowfor experimenting with different sensors and multiple sensors platforms. This step should bedone since the Arduino boards offer many possibilities that are not yet investigated. A GPSshield with the ability to connect to the Arduino board and give simultaneously position andclimate parameters measurements was one of the applications that was not finally tested dueto limited time. However, such a technology would be really pioneering since it would solvepartly the synchronization problems between the devices, i.e. at least the ones related to GPSmeasurements and climate measurements. The characteristics of this GPS device that can beattached to the Arduino are given in the Appendix A.7.
44
Chapter 5
Building a 3D environment forurban climate research
The second contribution of the Synthesis Project to the CCC is about modeling the campus in a3D environment. This 3D environment will act as a central storage place for all data generatedby TU Delft urban climate research initiatives. It will contain a high quality, multi-level ofdetail 3D representation of all significant objects (buildings, trees, green areas, water bodies,etc.) located on the campus. The 3D campus, or parts of it, can be used, amongst others, forwind and heat simulations. The 3D environment will also be able to store measurements madeby sensors scattered across the campus.
This chapter is structured as follows: section 5.1 will prioritize the requirements from section3.2 which are applicable to the development of the 3D model. Section 5.2 willl discuss available3D modelling techniques as well as the available data for this project and how it and the 3Drepresentations will be stored. Section 5.3 will discuss how the 3D framework is built and whichdecisions have been taken in view of the stakeholders’ requirements.
5.1 Requirement analysis
As explained in section 3.3, the stakeholder requirements are to be prioretized using the MoSCoWmethod. Following is a MoSCoW priotirization of the stakeholder’s requirements as identifiedin section 3.2 (table 3.1).
MUST
Store city object geometry Enabling climate research requires knowledge about the loca-tion, orientation and types of objects on the campus. These parameters have influence onthe energy balance of a city and the behaviour of wind. The following objects must bestored explicitely: buildings (as block models i.e. simple extrusions), streets, waterbodies,grass and trees.
Store surface properties: roughness, reflectivity, heat capacity Surface parameters haveinfluence on heat exchange processes and wind dynamics. Surface parameters must bestored for all surfaces stored in the model. Note that, for instance, roof surface parameterswill be different from wall surface parameters.
Store measurements The 3D framework is to act as a centralized storage location for alldata generated by urban climate research at the TU Delft. Measurements collected bysensors are part of this data.
45
Store trees along with releveant climate parameters Trees and other vegetation have asignificant influence on temperature and humidity in their neighbourhood (see section2.3.1). Trees furthermore influence wind flows and act as pollutant filters. NDVI andLeaf Area Index are examples of tree parameters which can be used to analyse the effectof trees in an area.
Extensible The 3D model must be easy to extend i.e. it must be easy to add new objects butalso have the ability to add new object parameters.
Use standards for data storage and exchange Using standards will ensure easy and openaccess to the stored data.
Store time The 3D framework must be able to store time. The reason to store time is twofold.First, collected measurements are a function of time or position and time. Second, bystoring time one will be able to compare situations from the past to situation in thefuture.
SHOULD
Store roof geometry The model should be able to store not only block models, but alsomodels with a higher level of detail e.g. buildings with distinct roof shapes.
Use TU Delft data Using in-house data is a showcase of TU Delft’s abilities to create highquality data. It also means that the data producers are close by.
Adaptable to different simulation scenarios The model should be able to service differentsimulation scenarios such as performing analysis on the campus with and without certainobjects.
Real-time measurement storage The model should be able to store real-time measure-ments.
COULD
Store buildings facades Building facades have an influence on local temperature (i.e. due toreflectivity properties). As such they will be modelled.
Easy data input and output Easy data import and export will ensure that users will actu-ally use the system.
User manual Provide a user manual that explains how to import and export data from theframework.
3D reconstruction manual Provide a user manual that explains how to reconstruct newbuildings from laser scans, topographic maps and other available data.
Feedback capability To ensure end-user satisfaction, the framework should provide userswith the option to give feedback on the performance of the framework.
WON’T
Simulations The 3D framework will be able to perform simulations.
Web-based interface The system will have a web-based interface to query and view data.
46
5.2 Available models, data sources and storage methods
Several different 3D modelling and storing methods exist. This section will elaborate on someof these, give a list of already available data and discuss several data storage formats.
5.2.1 Models
A 3D model is an abstract representation of the real world. In this 3D model the real worldis represented by objects, attributes and relationships. The scheme of building a model isillustrated in figure 5.1. In the abstraction phase there is need to simplify, generalize andaggregate the information about the real world into the model. In this section the rules forsimplification, generalisation and abstraction are explained in detail Tsichritzis and Lochovsky(1982).
Figure 5.1: Scheme for modelling the real world
In the abstraction real world features are represented by objects and primitives. Objects ina 3D model are constructed using simple objects which are described in 0D (point), 1D(line),2D(surface) and 3D(volume). A ”complex” object like a dice, is a combination of simple objects(3D solid cube and 2D image of the dots). These objects are represented in 3D model bygeometric primitives. Primitives can be subdivided in surface-based and volume-based elements.
PrimitivesSurface based : modelling the skin:- Point, line, polygonVolume-based : modelling the interior:- Spheres, cubes, cylinders etc.
Real-world objects are represented in a digital 3D model by using common representationschemes, such a scheme consist of a set of primitives as explained before. Again the schemes arevolume- based and surface-based. Three common representation schemes are explained nextand the most relevant one is chosen.
5.2.1.1 Voxel models
The voxel approach is related to the volume-based way of modelling. Solids consist of volumetricelements called voxels which are organised in a 3D grid format. A voxel is a volume element(3D pixel), a 3D cubical holding one or more data values. In figure 5.2 a voxel representationof a teapot is shown.
47
Figure 5.2: Modelling a teapot using voxels (Washington University)
Pro’s Con’s
+Enable modelling of continuous phenom-ena: geology , body etc
-Many data for high resolution
+Data is structured regularly - The surface is always discrete i.e. it looks”rough”.
5.2.1.2 Constructive Solid Geometry
Another volume-based method is CSG, in which the model consist of primitive solids like cubes,spheres and cylinders . The representation is a combination of primitive solids, where primitivesolids are subjected to operations like union, intersection and difference (See figure 5.3). Theobject is a CSG tree with operations at inner nodes, primitive solids at leaves.
Figure 5.3: Modelling an object using CSG
Pro’s Con’s
+Easily constructed in computer-aideddesign.
-Relationschips between objects might bevery complex
+Easy to compute the volumes. - Real world objects may get very complex
5.2.1.3 Boundary representation
This modelling technique uses surface elements to reconstruct real world features. Bound-ary representations also called B-reps and are made of an organised collection of boundinglow-dimensional elements. These low-dimensional elements can be divided into simple B-repsconsisting of planar faces, straight edges and complex B-reps like curved surfaces and edges.3D surfaces constructed by these low-dimentional elements are shown in the figure 5.4. B-repscan model the objects in 0-3 dimensions.
48
Figure 5.4: Modelling building using B-reps
Pro’s Con’s
+Appropriate for real-world visible ob-jects: measurements of properties that arevisible
-Boundary representations are not unique,in the sense they can be reconstructed indifferent ways.
+B-reps primitives are usally used by therendering engines
- Constraints may get very complex
From the three discussed approaches the boundary representation is the modelling techniquemost relevent to this project. Climate parameters interacts mostly with the limits of objects(surfaces), insides of objects is not of primary interest in climate research.
5.2.2 Available data
This section will discuss the data used in the Synthesis project.
5.2.2.1 Laser scans - AHN
In the last two decades airborne and terrestrial laser scanning have become an important sur-veying technique for the acquisition of geo-information. A large variety of instruments andtechniques are available, a distinction can be made between airborne and terrestrial laser scan-ning. The high quality 3D point clouds are nowadays used for the production of digital terrainmodels and 3D city models. This part of the project will investigate and implement a methodto extract trees and their modeling parameters.
FLI-Map is the acquisition system of the AHN2 data in the province where the TU Delft islocated. The FLI-Map 400 is a helicopter mounted ALS system, designed to acquire highlyaccurate and detailed terrain features. The laser is capable of 150.000 - 250.000 pulses perseconds and the scan angle is more than 50◦ − 60◦ with a range accuracy of 1 cm (1σ). Thepositioning is done with two L1/L2 GPS receivers which results in a highly accurate position.The accuracy of the laser is 1 cm, which is reduced to 3 cm (1σ) due to the errors introduced bythe Inertial Navigation System (INS) and GPS. The point density is a function of flight speedand altitude and varies between 10 and 100 points per square meter Fugro (2010).
Actueel Hoogtebestand (AHN2) Airborne laser scan data is acquired nationwide since1997, by this means an area of 33.800 square kilometres was sampled. This first grid containeda varying point density of one point for each 16 to 1 square meter. A new campaign startedin 2007, to increase the point density and accuracy, made possible by technical and financial 1
improvement. The new campaign resulted in AHN2; a more dense sampled space with a meandensity of 10 points per square meter.
1due to the enlargement in density, these measurements can now be used by Water Management Authorities,by this means they don’t need to assign there own ALS campaigns
49
Figure 5.5: FLI-MAP system in action (Fugro, 2010)
Before the campaign of AHN2 was approved to fly nationwide, a test pilot was done in Zeeland.The results of this test case were within the mission requirements. The results of this studyshowed a precision of an individual point in the order of 5 cm (1σ), on hard topography. Whilea systematic deviation was in the order of again 5 cm Swart et al. (2007).
When the AHN2 data is to be used for parameter estimation of trees, the acquisition techniqueshould fullfill a certain degree of detail. The acquisition by ALS was done during a leafless period(end of January till March 2008). This means the laser can penetrate more easily through thetree onto the ground surface. In effect the laser only contacted branches, so by consequence anestimate of the tree canopy porosity may be hard to do. A closer look to AHN2 data of thecampus, showed the appearance of trees in high quantities.
Figure 5.6: A cross-section of AHN2 data from around the TU library, trees are clearly identi-fiable, as is the spike of the library.
The use of ALS to estimate parameters for trees like: leaf-area-index, or tree crown diameter isdemonstrated (resp. Farid et al. (2008) & Popescu et al. (2003)). However these studies weredone in a favourable situation with leaves and such (e.g.: additional terrestrial measurements,multi response laser, etc. ).
The flight strips of the campaign are merged together by information of the acquisition centreand point cloud adjustment. After the network adjustment, the data is split into two subsets.One contains the ground level, while the other contains the other points. The separation intoboth files is done semi-automatically. The resulting files are stored in the LAS-format. Theseconsist of x, y and z coordinates. Other information as acquisition data or intensity is excluded.
5.2.2.2 3D models
Extensive use has been made of existing 3D data. Following is a list of available data sets.
• A topologically correct extrusion model of buildings in the campus.
50
• An old digital terrain model of the campus.
• The interior of the OTB building.
• Textured models of several TU Delft buildings.
5.2.2.3 Other data sets
Large-scale base map The Dutch large scale base map (GBKN) has been used to extractthe locations of the campus trees. Parts of the GBKN have also been used as basis for terrainclassification.
TOP10NL TOP10NL is an object-oriented topographical data set. TOP10NL has been usedto identify waterbodies on the TU Delft campus.
5.2.3 Storage formats and methods
A major requirement placed on the 3D model by the stakeholders is that it should be easy toextend. To achieve this adaptability, a data storage mechanism must be found which is able tofacilitate this. Furthermore, the data storage method should be able to not only store geome-try, but also thematic and sensor data which are needed for performing accurate simulations.Following is a short research on the available 3D data storage formats and a discussion on theirapplicability to the 3D model based on Zlatanova (2010).
5.2.3.1 Drawing Exchange Format (DXF)
AutoCAD DXF is a CAD data file format developed by Autodesk for enabling data interoper-ability between AutoCAD and other programs. DXF supports different geometries, layers anddrawing attributes. It does not support thematic attributes or topological attributes. DXF hasbeen a closed format for some time, Autodesk did not publish specifications for several years,correcting imports of DXF becomes difficult; DXF is not designed for visualization on Internet.Mainstream software that support DXF are ArcMap, AutoCAD, Maya, Microstation, GoogleSketchUp Pro.
• Strong in geometries• No thematic and topology are stored• Not for web-browser visualization
5.2.3.2 Shape (SHP)
ShapFefile is created by the Environmental System Research Institute (ESRI). A shapefile storessimple features and their attribute data. The geometry of a feature is stored as a set of vec-tor coordinates. Shapefiles can store points, lines and polygon features. Shapefiles do notstore topology explicitly. Rather, topology is derived on run time in the software. Sinceshapefiles store simple features only, they tend to be suited for visualization and editing ca-pabilities. Mainstream Software that support this format are ArcInfo, SDETM, ArcView GIS,BusinessMAPTM.
• Strong in geometries• Stores thematic attributes• Not for on-line visualization
51
5.2.3.3 Virtual Reality Modelling Language (VRML)
VRML: Virtual Reality Modeling Language is a standard file format for representing 3D inter-active vector graphics, designed particularly with the World Wide Web in mind. It allows tospecify dynamic 3D scenes through which users can navigate with the help of a VRML browser.VRML is a text file format, in which geometric features like edges and vertices can be specifiedalong with the mapped texture, surface color, transparency, and so on. VRML has many pos-sibilities to design/store a realistic scene; it has simple topology, but it can not store thematicproperties. In addition it is a web standard for exchange of graphics on the internet.
• Good visualization results in large files• Surface attribute can be stored• Thematic attributes can not be stored• Well integrated with the world-wide-web
5.2.3.4 Keyhole Markup Language (KML)
KML is an XML-based language schema for expressing geographic annotation and visualizationon Internet-based, two-dimensional maps and three-dimensional Earth browsers [5]. KML fo-cusses on visualization and user interaction. It supports geo-referenced images and 3D shapesin different styling options with textual marking capability.
• No thematic attributes• Web-based maps• Good at visualizing geographic features (2D, 3D, textual attributes) and coordinates
5.2.3.5 CityGML
CityGML (Groger et al. (2008)) is an information model for the representation of 3D urban ob-jects. It defines the classes and relationships of objects in cities with respect to their geometrical,topological, semantical and appearance properties. CityGML has the following properties
• stored objects have geometry and semantics• objects are stored as themes where each theme has a set of properties• geometries are stored as surfaces with each surfaces having a set of properties• is modelled in UML therefore easy to extend with new classes and parameters• exhibits four Levels Of Detail(LOD), both for building and terrain representation
CityGML is an object-oriented information storage model i.e. a super class exists which storesinformation about the scene as a whole while its subclasses store information about the indi-vidual objects, for example, buildings. Each building in turn is a superclass, an aggregation, ofits windows, doors, walls, etc. The classes and relations between them are modelled using theUnified Modelling Language (UML).
The power of CityGML comes from its ability to store thematic information about all storedobjects. Each object in CityGML has a known type i.e. each object is either a building, a tree,a road, etc. Next to thematic data, CityGML can store surface parameters which define theappearance characteristics of objects. Also, with CityGML it is possible to store the geometricrepresentation of an object in different levels of detail. Level of Detail 1 (LOD1) for buildingsis a simple extrusion model (i.e. buildings have flat roofs and facades). LOD2 is an extrusionmodel with has complex roof shapes. LOD3 buildings have textured, complex facades andrealistic roof shapes. LOD4 buildings have an interior.
After comparing CityGML’s properties to the requirements prioritized in section 5.1 i.e. storecity object geometry, surface parameters, extensibility, usage of standards and noting its supportfor different LOD’s, CityGML is selected as the right data storage model for this project.
52
The Spatial Model displayed on top of figure 5.7 describes the relations between the differentprimitives and how they aggregate to more complex geometries. A zero-dimensional object ismodelled as a point, a one-dimensionalobject is modelled as a curve and so forth.
Figure 5.7: An overview of the CityGML hierarchy. Every object stored in CityGML has ageometry (described in the Spatial Model), an appearance i.e. surface properties (described inthe Appearance Model) and is of certain type (described in the Thematic Model).
The Appearance Model provides means to store and manage appearance attributes of anobject. This model provides information about the observable properties of surfaces. Thesecan be visual-only properties like textures and colors but also material properties (shininess,transparency, smoothness, etc.). It is in this model that the static climate parameters identifiedin section 5.1 (i.e. roughness, heat capacity and reflectivity) can be stored.
CityGML stores the objects in the built environment in so-called themes. Each theme modelsa different type of object. For instance, the building theme stores spatial and non-spatial dataabout building objects. Other themes are transportation, waterbodies, landuse, vegetation,etc. These themes are defined in the Thematic Model. It is in this model (as a subclass ofVegetationObject) that trees and their climate parameters are stored.
The base class for all thematic classes of CityGML is the abstract class CityObject which isdefined in the Core model. CityObject provides names for all objects, their creation andtermination dates and links to external references. Several CityObject features can be groupedto a CityModel.
The Core Model is the last aggregation step in the object definition process. When defining anew object in CityGML one has to first define its geometry as defined in the Spatial Model.Then the appearance should be modelled as defined in the Appearance Model. Then the objectshould be assigned a theme as defined in the Thematic Model.
Storage CityGML is traditionaly stored in files in GML 3.0. The drawback of file storage isthe lack of flexibility: to add or remove buildings the whole file must be read each time. Also,file storage does not allow multiple users to work with the data at once. Lastly, accessing themodels requires that the user has physical access to the files.
An alternative method of storing CityGML is by using a spatial database management system.A spatial database does not exhibit mentioned limitations i.e. it supports multiple users, usesspatial indices to quickly access data, has a standard way of accessing data through SQL,
53
etc. Kolbe et al. (2009) have transformed the CityGML classes to tables in an Oracle Spatial11g database. All relationships between CityGML classes have been modelled as relationshipsbetween tables.
A spatial database gives the possibility to perform spatial queries e.g. calculate distancesbetween objects, calculate buffers around buildings ,etc. Such operations come in handy whenplanning new recreational areas and parks in a city. Using buffers, urban planners can forexample, determine the number of people living within walking distance from of parks.
Storing CityGML in a database amounts to filling the right tables and respecting their relation-ships. This can be done in several ways: through the provided importer/exporter tool, throughthe software package Feature Manipulation Engine (FME) or through Python code. In thisproject all three methods have been tried with varying success rates.
The provided importer/exporter tool works fine for standard CityGML. It stops exporting onceCityGML is extended (this has been done for trees and is described in section 5.3.1.3). Theprovided importer/exporter has been used for the buildings as these are already available inCityGML.
The software package FME is a powerful yet complex data transformation tool which is able toconnect to Oracle Spatial. FME has been used to import the landuse data as this was availableas a Shapefile.
Python has a straightforward method for database connections. Once the connection is madethe database can be filled with plain SQL statements. Python was used for importing the treessince these came in a non-standard format. Python also has to be used for all extensions sincethese are not supported by the standard importer/exporter.
5.3 Reconstruction and storage methodology
3D reconstruction of real-world objects from laser scan data is a thoroughly researched topic.Extensive research has been performed in the fields of building reconstruction and tree recon-struction. This section discusses different methods for tree, building and terrain reconstruction,states how a specific method has been implemented and how the result is stored in the database.
5.3.1 Trees
Section 2.3.1 discussed the importance of trees for city climate. Trees create , and reduce the areawhich is illuminated by the sun. Although trees are dark in the visible range of the spectrum,they help cool their surroundings by reflecting almost all of the near-infrared solar radiation.Trees provide an additional cooling effect by evapotranspiration, reducing the temperature ofthe surrounding air. Carbon dioxide is used during the process of photosynthesis. In this processcarbon dioxide is converted into carbon. By taking carbon dioxide from the atmosphere, theamount of green house gasses is reduced. Trees have also an influence on the wind, partly thewind is deflected or penetrates the canopy, this results in a lower wind speed.
This section describes tree parameters that can be of importance for city climate research. Amethod is described how to extract these parameters from laser scans and to which dimen-sion they belong. This is discussed for each parameter independently and incorporated in thedatabase.
54
5.3.1.1 Theory
First the extraction and reconstruction of the tree geometry from airborne laser data is de-scribed. Then a methodology is proposed to determine the Wood Area Index (WAI) from laserdata and how this index can be used to determine the Leaf Area Index (LAI). Another pa-rameter of importance is the Normalized Difference Vegetation Index (NDVI), this parameteris estimated through measurements of a optical remote sensing instrument. Lastly a method isgiven for the determination of tree drag coefficients based on their species.
Geometry reconstruction of trees There are various ways to reconstruct trees based ona 3D point cloud. In the literature many methodologies are described varying from highlydetailed to extremely simplified descriptions. For this research it is important to find a rightbalance between simplification and functionality. Geometric paramaters that are of importanceare mostly concerned with the canopies covering volume and frontal area. Thus the outerdimensions of the canopy are of importance. The exact location of the stem, is of limitedimportance. It’s influence is negligible compared with the branch and leaf coverage.
Tree parameters of importance to climate Apart from the geometrical measures, otherattributes can be considered. This information should be of importance for climatic modelling.There are some coefficients that could be of importance, they are discussed below. Togetherwith a manner to retrieve them from data.
Leaf Area Index (LAI) The area covered by all leaves of a tree in respect to its groundcover of a tree is a ratio called Leaf Area Index [m2/m2]. This measure is about 2 to 5 forfully grown trees. A high LAI is equal to a densely leaved tree, so as much sun can be caughtby the leaves for photosyntheses. Due to this compactness there is a lot of shadow and drag.Consequently one can state that this measure can be of importance for both shade and windanalysis. Estimating this parameter is a challenge, as a direct measurements are difficult andtime consuming.
Normalized Difference Vegetation Index (NDV I) NDVI is defined as follows:
NDV I =NIR−RNIR+R
(5.1)
where NIR is the near-infrared band and R is the red color band. Research has shown that theNDVI is directly related to the photosynthetic capacity and hence energy absorption of plantcanopies Myneni et al. (2002). NDVI is ussually shown in raster value maps varying from 1 till-1 covering large areas. In this case it is represented per tree, and interpreted as a tree climateparameter.
Drag Coefficient (Cd) The drag coefficient is a dimensionless quantity, and Mayhead (1973)gives the computation of the drag coefficient as;
Cd =D
1/2ρU2A(5.2)
With:
• D [kg], the force or drag on the tree.
55
• ρ [kg/m3], density of air.
• U [m/s], being the wind speed.
• A [m2], is the frontal area of the tree.
The drag is a function of the leaves, branch geometry and its organic material. The distinctionof this coefficient is mostly grouped in types of trees, as is illustrated in the table 5.1.
Species Cd Spieces Cd
Grand fir 0.36 Scots pine 0.29Sitka spruce 0.35 Douglas fir 0.22
Norway spruce 0.35 Lodgepole pine 0.20Corsican pine 0.32 Western hemlock 0.14
Table 5.1: Tree species and their coefficent Mayhead (1973)
Hoewever,the ability to identify tree species through airborne laserdata is difficult.
5.3.1.2 Implementation
The extraction of trees out of the AHN2 was done through the availability of the tree locations.These locations were derived from the large scale basemap (GBKN). The tree centroids were thestarting point for selection of a subset of the laserscan data. All laserpoints within a radius of15 meters from the centroid were selected, this with the help of a k-D tree query. The resultingselections did contain other objects; other trees, cars, hedges, buildings etc. These unwantedfeatures were exclude by a supervised k-means clustering method.
A K-means clustering is an operation where the algorithm finds a predefined number of clusters.This is done in a recursive manner. The algorithm consist of four steps. In step one the ’initialmeans’ are defined, then in step two the clusters are created based on these initial means. Instep three the centroid of each of the k clusters becomes the new means. Step two and three arerepeated until there are no more changes Wikipedia (2010f). The remaining includes outliers,these were excluded by a Median Absolute Deviation filter.
The resulting pointcloud got assigned to a tree entity. With these pointclouds it is possibleto extract characteristics of the vegetation. This link is done through statistical measuresStraatsma and Middelkoop (2007), i.e.: 1st-4th moment, median, D10 - D90 etc. Though theseare only global statistical measures, and the relations differ on regional base, as tree types adjustto the local climate. But the statistical measures can be useful to construct a three dimensionalrepresentation of a tree. Unfortunatly the shape of a tree is family dependent, a uniform shape(e.g. ellipsoid, cilinder) is not a good simplification for all tree types. Thus to have a betterrepresentation of the real world a three dimensional convex hull was calculated Barber et al.(1996). Resulting in a subset of all outerpoints and an estimate of its covering volume. Figure5.8 shows the result.
To integrate more parameters into the model, a survey was conducted on the trees around theOTB building. By sampling the leafs, it is possible to classify the individual trees by theregenus and species. Through this knowledge it is possible to identify more properties of thesetrees. For example the Cd-value, the drag coefficient. These coefficients were taken from Laversand Moore (1983) and Wessolly and Erb (1998). Together with the frontal area of the tree (A),which is calculated by the area of the convex hull in x and y direction.
The Leaf Area Index (LAI) is defined as
56
Pointcloud Convex hull
Figure 5.8: The shape of the reconstructed trees is represented by their convex hulls
LAI =1− wpwp
×WAI (5.3)
where the WAI is the Wood Area Index which is defined as,
WAI =N/2×Af ×A−1
lasercoverage
Atree(5.4)
If this formula (derived in Appendix B.2) is applied to the dataset the estimates are in the orderof 0.01. This is a factor of 100 too low. An attempt to estimate WAI and LAI direclty from thelaser points was made, but this requires additional research.
The NDVI raster map (figure 5.9) is obtained via Quickbird data. The tree positions areobtained from the GBKN and based on that knowledge circles of 1m diameter are created.With zonal statistics the mean NDVI value per tree is obtained and stored in the database.This can be seen in figure 5.9.
Figure 5.9: NDVI raster value map around OTB, the blue circles indicate the tree locations.
57
5.3.1.3 Storage
Trees are stored in CityGML’s Thematic Model as Solitary Vegetation objects. Each Soli-taryVegetation object has a geometry and several other properties (height, trunk diamter,species, etc.).
A Solitary object does not, however, have properties which resemble the Leaf Area Index orNDVI. To accomodate these, CityGML has been extended with a new class called Vegeta-tion Additional Para which has been related to the SolitaryVegetationObject. Also, a list calledVegetat Dictionary has been defined which holds some additional properties. (figure 5.10)
Figure 5.10: The extended Vegetation Model UML diagram. The class Vegeta-tion Additional Para has been added
As discussed in the previous section, trees are represented by their convex hulls (figure 5.8).These convex hulls are stored in the database. Figure 5.11 shows a tree in LOD2. As explainedin section 5.2.3.5, storing geometries of objects in the database involves updating several tables.Appendix B.1.1 describes how to fill which tables in more detail.
Figure 5.11: The representation of a tree in LOD2.
58
5.3.2 Buildings
As discussed in section 2.2, buildings are one of the main causes of the Urban Heat Islandeffect. Section 2.3.1 discussed the static climate parameters which have an influence on theurban climate. Buildings contain the majority of these static parameters (building facades,surface properties (color, heat, reflectivity, etc), roof types, etc.). As such, buildings will bestored in the database.
This section discusses several reconstruction methods based on a literature study. Then theimplementation and storage of the reconstructed model is described.
5.3.2.1 Theory
Automatic building detection and reconstruction from remotely sensed data has been a hot topicsince the early 1990’s. Early methods use a single photograph for the detection of buildings.Although a relatively cheap and easy technique to apply, it has as major disadvantage the factthat buildings may be partially visible due to occlusion by other buildings, trees or shadows.Using a single photograph therefore only gives satisfactory results when buildings are placed farapart. For example, Lin and Nevatia (1998) detect buildings by observing their shadows on theground. They assume that the buildings have flat, rectilinear roofs and that the shadows theycast fall on flat ground. These assumptions are clearly not valid for highly populated, highlycomplex urban areas.
A solution to the occlusion problem is to increase the amount of available data by using severaldifferent images of the same building. At the same time this allows for height measurementsusing stereo-photogrammetric methods. Fischer et al. (1998) propose a model-based methodwhich uses multiple images to recognize simple geometries (points, lines and regions) whichbelong to a building. They then aggregate these, using pre-defined rules, to form more complexgeometries (e.g. corners, wing, faces) which in turn are aggregated to form building parts. Thehigh complexity of urban areas may prevent this method from detecting a satisfactory number ofsimple geometries and may therefore be unable to complete the process. Baillard et al. (1999)describe a reconstruction method which uses a sophisticated line matching algorithm whichextracts 3D lines from several images. These lines are then used to generate 3D planes which inturn are assigned to buildings. However, due to the complexity of urban areas buildings may beoccluded by shadows on all gathered images. Edge detecting algorithms, and all steps dependingon them, will fail. When automatic building reconstruction methods fail one may, when dealingwith small data sets and regions, decide to turn to semi-automatic methods. Maarten Vermeij(2001) describe a reconstruction method in which the user has to identify all corner points of thebuilding under consideration. The algorithm is then able to generate the model of the building.
Recent years have seen an increased use of LIDAR systems as a remote sensing technique forthe reconstruction of terrain and buildings. LIDAR systems deliver a highly dense and geo-referenced point cloud. The high density and the fact that the height of all points is measured(as opposed to inferred) makes point clouds the preferred data source for building detection andterrain reconstruction.
Wang (2000) use a LIDAR data generated surface to detect buildings by detecting and classifyingedges. Building edges are distinguished from non-building edges by observing their geometryand shapes based on orthogonality, parallelism, circularity (defined as the ratio of an edge’slength over its area) and symmetry. Next, a TIN is built from all points contained in the foundedges. The last step is the creation of surfaces from the TIN triangles which are intersected tofind the corners of roofs.
The concept of intersecting roof planes is also used by Maas and Vosselman (1999). They firstcluster all points which belong to the same plane and then perform a Delaunay triangulationto obtain that plane. The so obtained planes are intersected with one another so as to obtain
59
the edges of the roof. The walls are then extruded from the edges of the roof to the terrain.One can imagine that this building reconstruction method will only work for roofs with nooverhang. To battle this problem, one may decide to add 2D GIS data to define the position ofthe walls. Vosselman (2001) uses building plans in the roof detection step by assuming that aroof edges run parallel to ground plan edges. N. Haala (1998) propose a model-based buildingreconstruction method which uses constructive solid geometry (CSG) to represent buildings.CSG representation aggregates simple primitives into more complex structures to representcomplex building shapes. In this method, ground plan information is used for the automaticextraction of said primitives. Ground plan information is also used in the detection of roofplanes by grouping all calculated normals which are in the direction of the ground plan.
Finally, ground plans may not only be used as support for a building detection method but alsoas a starting point. Ledoux and Meijers (2009) use ground plans combined with point cloud tocreate extrusions of buildings. Although a simple process, the novelty of their method is thefact that they create a topologically correct model of the reconstructed scene. This is valuablewhen the model is to be used for more than visualization purposes.
5.3.2.2 Implementation and storage
The extrusion model created by Ledoux and Meijers Ledoux and Meijers (2009) is readilyavailable since Ledoux and Meijers are employed by the TU Delft. As such it meets the in-house SHOULD MoSCoW requirement stated in 5.1.
As the model is available in native CityGML it is easily imported in the datbase using the dataimporter/exporter developed by Kolbe et al. (2009).
5.3.3 Terrain and landuse
Like trees, terrain plays a major role in urban climate. Its geometry has an influence on windpatterns while its type (grass, water, asphalt, etc.) has influence on the amount of heat beingabsorbed and reflected and the amount of water being evapotranspirated.
The terrain geometry is reconstructed from laser scans (AHN dataset). The terrain type isobtained from the Dutch large scale base map (GBKN) and TOP10NL. This section describesthe theory of terrain reconstruction, how this theory is implemented and how the terrain andthe landcover information are stored in the database.
5.3.3.1 Theory
A DTM is a continuous field in which for every location (x, y) an elevation value is eitherknown or estimated based on the known values. The calculation is conducted by interpolationmethods.
A typical interpolation method is the triangulated irregular network (TIN) which is using De-launay triangulation. The advantage of modeling a DTM with a TIN is that the number ofpoints stored is less than a grid-based data structure. This implicates that in areas with littlevariation in elevation the points are more scattered than in areas with great variation. Anotheradvantage is the existence of TIN-based algorithms to increase the number of vertices insertedin the TIN and therefore the level of detail, by extension of a lower detailed TIN with addingvertices (M. de Berg (1997)).
For the purpose of the project the DTM is a useful tool to generate relief shading in the Mekel-park at the TU Delft campus with respect to temperature measurements Zhou (1992). Anotheraim of the DTM is to support near-surface wind speed calculations which will increase with an
60
increase in height. Furthermore the water flows can be simulated which can be important tomodel drainage. The third argument for including the surface is for the 3D visualization of theTU Delft campus. The DTM adds more detail to the 3D model.
5.3.3.2 Implementation
For the processing of the AHN-2 data, different software is used. First of all, LP360 viewer isused to visualize the dataset. The colors represent the elevation values (figure 5.12). The pro-gram used to create a TIN is LAStools (University of North Carolina, Department of ComputerScience). The TIN is stored in a text file which includes the point numbers of the three verticesincluded in the triangle. For CityGML, the file needs to be converted to a text file with thex, y, z coordinates of the vertices (comma-separated). Therefore a python script is written. Inshort, the following three phases can be distinguished for creating a TIN-based digital terrainmodel:
1. Selecting parts of AHN-2 data
2. Create TIN
3. Convert TIN to suitable CityGML format
Figure 5.12: Subset of AHN2-terrainfile covering the Mekelpark.
Selecting parts of AHN-2 data based on RD coordinates The complete AHN-2 groundlayer dataset consists of approximately 23.4 × 106 elevation values. Only the areas of interestfor the 3D model will be considered and therefore only points apparent within the areas of OTB(1), Mekelpark (2) and the TU campus (3) are included for further processing (see figures).After selecting the lidar points in the three areas of interest, the number of points included iscalculated (see table).
61
Creating a TIN with LAStools The processing of lidar data to a TIN is performed byan already existing program LAStools. The algorithm functions quite well with large datasetsand results in a TIN within very reasonable time. However, the program cannot deal withconstraints. This implicates that the footprints of buildings are also included in the triangulationi.e. they are lost since the triangulation does not take them into account. This has to be takeninto consideration for further research on this topic.
5.3.3.3 Storage
As mentioned above, the terrain consist of geometry and landuse information. The terraingeometry is stored as a CityGML Surface Geometry in the database as explained in appendixB.1.4. The landuse information is stored using CityGML’s Land Use model as explaiend below.
Landcover data is obtained from the Dutch large scale base map (GBKN) and the vectorizedtopographic dataset TOP10NL. The waterbody, building, roads and grasslands classes are ex-tracted by using GIS software QGIS. The grass and water classes are merged into one bigfeature, because their surface characteristic on the used scale is more or less homogeneous, thesame holds for the water objects. Different roads are not merged, because each road may haveits own surface characteristics. These datasets are then merged into one dataset and loadedinto Oracle by FME software.
5.3.4 Measurements
One of the phases of performing climate research is doing measurements Oke. The methodfor obtaining measurements is described in chapter 4. As stated there, the Synthesis projectfocuses on the example temperature measurement. Section 4.4.1 states how the measurementsare made and section 4.4.2 in what format they are stored. This section will discuss how themeasurements are modelled in CityGML and then stored in the database.
As described in section 2.3.1, the parameters influencing urban climate can be catagorized intodynamic and static. Static here implies that the parameter does not vary significantly over time(e.g. building surface parameters, land use, etc). Dynamic parameters do vary over time andthese variations are often related to other dynamic climate parameters (e.g. sun radiation).Different storage methods can be applied for static and dynamic climate parameters.
Static climate parameters Storing static climate parameters is generally less memory con-suming than dynamic parameters, as these are invarient over time and have a fixed reference toan existing object (wall) or medium. Static attribute like emissivity is a property of the surfacematerial, it can be stored in the database as an attribute of an existing city objects like thewall of a building.
From the different models defined in CityGML (section 5.2.3.5), the Appearance model textureattribute could be used for the static climate parameters. In this case the static parameters arerefered to the surface attributes of cityobjects. Static climate parameters are inherent propertyof an medium, fixed in time so it only has to be stored once.
Dynamic climate parameters Dynamic parameters generally require more memory thanstatic parameters due to the storage of the time dimension. Dynamic attribute like temperatureare not related to a certain object. It is a continous variable through space and time. However,these continous variables are stored as point measurement in the database.
There is no native measurements class in CityGML. There is however, a generic city objectclass which is designed to store the object/attributes which are not explicitly modelled in the
62
native thematic classes of CityGML(5.2.3.5). Each Generic CityObject class has a geometryand properties for the type, time, value etc. of the measurement.
The location of a measurement is then stored in the geometry representation (which can be any-thing the user chooses) of the Generic CityObject. The following Generic CityObject propertiesare used to store the actual measurement.
Type of measurement Description of the variable and the corresponding units in SI.
Time of measurement The time of measurement inDD/MM/YYYY/HH24/MM/SS
Value of measurement The value of the measurement.
Storing measurements the Generic CityObject class allows efficient extraction of individualmeasurements in space and time. It allows more flexibility in grouping the measurements andacquiring them. Appendix B.1.3 shows the database schema of a Generic CityObject.
5.4 Results
This section lists the contents of the database. Figure 5.13
• Digital Terrain Model: LOD1 geometry representation of whole campus area and a LOD2geometry representation of OTB area.
• Buildings: LOD1 geometry representation of all buildings. LOD2 geometry representationof the following buildings: Aerospace Civil Engineering and Applied Physics.
• Trees: LOD1 geometry representation around OTB with a position and height informationfor each tree. LOD2 geometry representation of the same trees including the following pa-rameters: height, species, NDVI, Wood Area Index, Drag coefficient, English/Dutch/Latinname of each tree.
• Measurements: position, accuracy, value (temperature), measurement time.
• Landuse: landuse data covering the whole campus (streets, water, grass, footprints ofbuildings).
5.5 Conclusions
The Synthesis project developed a 3D framework which is able to store representations of theurban environment and measurements made therein. The 3D framework will act as a centralstorage place for all climate research performed at the TU Delft. The 3D framework storesgeometries of buildings, trees and terrain.
Trees
The 3D framework is able to store trees in two different levels of detail: as a single polygon andas a Triangulated Irregular Network. Along with each tree values for its canopy porosity, LeafArea Index (LAI) and Normalized Difference Vegetation Index (NDVI) for different seasonshave been stored. Each of these parameters has three representations and their parametershave been reconstructed from aerial laser scans (AHN2). A lot more information (e.g. dragcoefficient for grass and buildings, height profiles, etc.) can be extracted from the laser scans.Due to lack of time, scarcity and complexity of extraction algorithms focus was kept on theextraction of canopy porosity, NDVI and LAI. The used aerial laser scans can be enriched bycombining them with terrestial laser scans and satellite images.
63
Figure 5.13: Objects stored in the database
CityGML
A spatial database with the CityGML schema has been used to store the geometries and the-matic properties of urban objects. For this project CityGML has been extended to supportclimate research. The CityGML tree class has been extended to store tree canopy porosity, LAIand NDVI measures. Extensions for surface parameters have to still be implemented. How-ever, made extensions are not supported by many CityGML viewers and importers/exporters.Custom software has to be written to handle these extensions.
CityGML is a powerful but complex data model. Users first need to have a basic knowledge ofits structure before being able to use it (for instance extract an object’s geometry) effectively.
Having different LOD’s is a step towards the facilitation of multi-scale climate (i.e. local andmicro-scale) simulations.
Sensors
The 3D framework is able to store measurements performed by static and mobile platforms. Itstores the coordinates, the measurement type, measurement value and time as a native CityGMLGenericObject. Since GenericObject is a native class it is recognized by CityGML visualisersand exporters.
5.6 Future work
Based on the results and conclusions, a guidance of further improvement and development ofthis framework is given as follows:
• Extend the spatio-thematic information, e.g. model the entire campus in level of detail 2and add more surface characteristics, this will broaden the scope of climate study in thecampus.
64
• Enrich the data resources, e.g. combine the aerial and terrestrial laser scanners, addphotogrammetric texture to the building, take remote sensing images into account.
• Develop a Graphic User Interface (GUI) preferably web-based. This enables easy accessto the system for users world-wide and manipulation of the spatial database and campus-wide sensor network.
• Provide input and output functionality in the GUI that supports different formats. Thisenables faster model extension and extraction.
• Develop a feedback function for this system, that collects the suggestions from developersand end-users and reports this to the system administrators.
• Deliver manuals, e.g. of GUI, I/O tool, 3D reconstruction and data storage, which explainshow to use the current product and do further development.
• Connect the database to the sensor network seamlessly, this enables the storage of sensormeasurements and, together with the GUI, monitor change of climate parameters in real-time.
65
66
Chapter 6
Conclusions
The objective of the Synthesis project is to provide a framework to enable environmental re-search in the campus. Within this framework, tools are created to support the observation andmodelling stage of climate research.
In the observation stage a method is developed for continuous tracking of sensors using twopositioning techniques: GPS and WiFi positioning. With these positioning techniques, mea-surements of climate parameters can be taken anytime and anywhere in the campus. For thepurpose of this project, measurements are traceable outdoors (not inside buildings). The exam-ple sensor used is suitable for measuring temperature. The positioning system used consists ofEkahau WiFi positioning system together with a high sensitivity GPS receiver. The positioningsystem combining WiFi and GPS proved to be feasible, but limitations of both positioning tech-niques still constrain the capabilities of the resulting product. Synchronization problems arisewhen combining the two techniques because of the different calculation times required. TheWiFi positioning system currently available in the campus proved to be insufficient optimizedfor positioning. The WiFi access points coverage is not enough for positioning at a distancelarger than 5 meters from the buildings. This is insufficient to cover the blindspots of GPS.
Solutions are proposed to reconfigure the wireless access points to optimize the positioning per-formance. The currently available access points are positioned in an aligned constellation, whilea zigzag constellation would be preferred for position estimates. Other positioning techniquescould be used complementary to the existing WiFi-GPS combination to enhance the perfor-mance.
For the modelling stage a 3D framework is developed to store 3D representations of the campusenvironment and measurements made. A spatial database is used with the CityGML schema,which can store the geometries and thematic properties of urban objects. CityGML is anextensible data model defined in UML. Extensions make it possible to include climate relatedparameters that not defined in the standard model of CityGML.
The 3D framework can store geometries and attributes of buildings, trees and terrain in differentlevels of details. Existing geometric models of the buildings are imported into the framework.The terrain and trees are reconstructed from aerial laser scans (AHN2 dataset). In this processthe raw data is prepared and processed to extract the geomatric representation and the relevantclimate parameters. Tree climate parameters of interest such as canopy porosity, leaf area index(LAI) and Normalized Difference Vegetation Index (NDVI) for different seasons are extractedand stored in the framework. Research in this project has shown that climate parameters canbe derived from aerial laser scans, but due to lack of time, scarcity and complexity of extractionalgorithms focus was kept on the extraction of trees and relevant parameters. For terrain thedigital terrain model is reconstructed into the framework. More parameters can be extracted
67
using different data sources as complementary to the aerial laser scans like terrestrial laserscansand satellite images.
Custom software is written to handle the extensions of CityGML and integrating the positioningtechniques with the sensor system. The programming language Python is used in this projectto provide the necessary software for both measuring and modelling part.
The current framework is able to measure dynamic climate parameters in the campus, exceptthe positions located in the blindspot of both GPS and WiFi positioning. The measurementsperformed by the sensor can be stored in a 3D spatial environment including the time (4D). Theframework can handle and store thematic spatial data like the geometry of trees, houses andterrain and their relevant climate parameters. Current users need to have a basic knowledge ofprogramming language and databases before being able to use the framework.
The future framework should be able to serve researchers without prior knowledge of databaseand programming languages to do their climate related research. In this project the devel-oped framework is still in an initial stage, the functionality of the framework is defined butnot completed in this project. Researchers cannot easiliy make their measurement using thecontinuous trackable sensor system. The measuring device needs to be developed such that it ismobile and able to support different sensors. The interface to store and extract spatial temporaldata needs to be designed in order to improve the accessilbility and usability of the framework.This eventually allows researchers to extract and import data into the framework without priorknowledge of database.
68
Bibliography
The american heritage science dictionary. Oct 2010. URL http://dictionary.reference.
com/browse/climate.
An overview of the ieee 802.15.4a standard ieee communications magazine, January 2010. 0163-6804.
H. Akbari, M. Pomerantz, and H. Taha. Cool surfaces and shade trees to reduce energy useand improve air quality in urban areas. Solar Energy, 70(3):295–310, 2001.
Arduino. Arduino open-source electronics board. http://www.aduino.cc.
C. Baillard, C. Schmid, A. Zisserman, and A. Fitzgibbon. Automatic line matching and 3dreconstruction of buildings from multiple views. International Archives of Photogrammetryand Remote Sensing, 32(3):69–80, 1999. ISSN 0256-1840.
C.B. Barber, D.P. Dobkin, and H. Huhdanpaa. The quickhull algorithm for convex hulls. ACMTransactions on Mathematical Software (TOMS), 22(4):469–483, 1996.
Arduino boards, 2010a. www.faketakemake.posterous.com.
Cool Components-Arduino boards, 2010b. www.coolcomponents.co.uk.
Ekahau. The Case for Real Time Location Systems, 2009.
A. Farid, DC Goodrich, R. Bryant, and S. Sorooshian. Using airborne lidar to predict LeafArea Index in cottonwood trees and refine riparian water-use estimates. Journal of AridEnvironments, 72(1):1–15, 2008.
B. Ferris. Wifi localization with gaussian processes. University of Washington, 2006.
Andre Fischer, Thomas H. Kolbe, Felicitas Lang, Armin B. Cremers, Wolfgang Frstner, LutzPlumer, and Volker Steinhage. Extracting buildings from aerial images using hierarchicalaggregation in 2d and 3d. Computer Vision and Image Understanding, 72(2):185 – 203, 1998.ISSN 1077-3142. doi: DOI:10.1006/cviu.1998.0721.
Fugro. Fli-map system technical information, September 2010.http://www.flimap.nl/download/leaflets/FLIMAP400technicalinformation.pdf.
gpsworld.com. Indoor message system evaluated, indoor positioning - gps world website, a.
gpsworld.com. Using gps and pseudolites for humanoid robot positioning, gps world website, b.
gpsworld.com/wireless. Centimeter-accuracy indoor navigation using gps-like gps world web-site.
G. Groger, Thomas H. Kolbe, Angela Czerwinski, and Claus Nagel. Opengis city geographymodelling language (citygml) encoding standard. 2008. URL http://bit.ly/aszyxh.
F. Kleijer, D. Odijk, and E. Verbree. Prediction of gnss availability and accuracy in urbanenvironments case study schiphol airport. In Location based services and telechartography II,2009.
69
Thomas H. Kolbe, Gerhard Koning, Claus Nagel, and Alexandra Stadler. 3d-geo-database forcitygml. 2009. URL http://opportunity.bv.tu-berlin.de/software/documents/32.
G.M. Lavers and GL Moore. The strength properties of timber. Dept. of the Environment,Building Research Establishment; HMSO, 1983.
Manh Hung V. Le. Indoor navigation system for handheld devices. Technical report, WorcesterPolytechnic Institue, 2009.
Hugo Ledoux and Martijn Meijers. Extruding building footprints to create topologically con-sistent 3d city models. Urban and Regional Data Management, pages 239–266, 2009.
Chungan Lin and Ramakant Nevatia. Building detection and description from a single intensityimage. Computer Vision and Image Understanding, 72(2):101 – 121, 1998. ISSN 1077-3142.doi: DOI:10.1006/cviu.1998.0724.
Lumiad. http://www.lumiad.com/, October 2010.
K.T.G. Dobrindt M. de Berg. Extracting buildings from aerial images using hierarchical aggre-gation in 2d and 3d. Graphical models and processing, 60:1–12, 1997.
Siyka Zlatanova Maarten Vermeij. Semi-automatic 3d building reconstruction using softplot-ter. Proceedings of the International Symposium on Geodetic, Photogrammetric and Satel-lite Technologies: Development and Integrated Applications, pages 305–314, 2001. URLhttp://www.gdmc.nl/publications/index.html.
Hans-Gerd Maas and George Vosselman. Two algorithms for extracting building mod-els from raw laser altimetry data. ISPRS Journal of Photogrammetry and RemoteSensing, 54(2-3):153 – 163, 1999. ISSN 0924-2716. doi: DOI:10.1016/S0924-2716(99)00004-0. URL http://www.sciencedirect.com/science/article/B6VF4-3WY9SWX-D/2/
bf77855623cc77357f6d18dc974569db.
J. Marshall. Creating and viewing skyplots, gps solutions. 2002.
GJ Mayhead. Some drag coefficients for British forest trees derived from wind tunnel studies.Agricultural Meteorology, 12:123–130, 1973.
P. Misra and P. Enge. Global Positioning System: Signals, Measurements and Performance.2006.
R.B. Myneni, FG Hall, PJ Sellers, and AL Marshak. The interpretation of spectral vegetationindexes. Geoscience and Remote Sensing, IEEE Transactions on, 33(2):481–486, 2002.
K.-H. Anders N. Haala, C. Brenner. 3d urban gis from laser altimeter and 2d map data.International Archives of Photogrammetry and Remote Sensing, 32(3):339–346, 1998. ISSN0256-1840.
T.R. Oke. Initial guidance to obtain representative meteorological observations at urban sites.World Meteorological Organization.
T.R. Oke. Initial guidance to obtain representative meteorological observations at urban sites.World Meteorological Organization Geneva, 2004.
T.R. Oke. Towards better scientific communication in urban climate. Theoretical and AppliedClimatology, 84:179–190, 2006.
M.L. Parry. Climate Change 2007: impacts, adaptation and vulnerability: contribution of Work-ing Group II to the fourth assessment report of the Intergovernmental Panel on ClimateChange. Cambridge University Press, 2007.
S.C. Popescu, R.H. Wynne, and R.F. Nelson. Measuring individual tree crown diameter withlidar and assessing its influence on estimating forest volume and biomass. Canadian Journalof Remote Sensing, 29(5):564–577, 2003.
70
T.B. Salcedo Rahola, P. Van Oppen, and K. Mulder. Heat in the city - an inventory of knowledgeand knowledge deficiencies regarding heat stress in dutch cities and options for its mitigation.Technical report, TU Delft, 2009.
H. Schreutelkamp and L. Strang van Hees. Benaderingsformules voor de transformatie tussenRD- en WGS84-kaartcoordinaten. Stichting De Koepel, 2001.
M. Straatsma and H. Middelkoop. Extracting structural characteristics of herbaceous floodplainvegetation under leaf-off conditions using airborne laser scanner data. International Journalof Remote Sensing, 28(11):2447–2467, 2007.
L.M.Th. Swart, S.J. Flos, and W.S. Zomer. Laseraltimetrie voor waterkeringbeheer. Ontwikke-lingen, gewenste specificaties, procesbeschrijving en evaluatie AHN-2-proef. STOWA Werk-groep grootschalig uitvoeringsproject laseraltimetrie (WGL), 1, 2007.
D. Tsichritzis and F. Lochovsky. DataModels. Prentice-Hall, Inc. New Jersey, New Jersey, 1982.
E. Verbree and S. Zlatanova. Positioning lbs to the third dimension. In Location based servicesand telechartography, pages 107–118, 2007.
George Vosselman. 3d building reconstruction from point clouds and ground plans. InternationalArchives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(3):37–44,2001. ISSN 1682-1750.
Z. Wang. Building extraction and reconstruction from lidar data. International Archives ofPhotogrammetry and Remote Sensing, 33(3):958–964, 2000. ISSN 0256-1840.
Washington University. Open 3d printing. http://open3dp.me.washington.edu/2010/07/
what-wee-really-need-next/.
L. Wessolly and M. Erb. Handbuch der Baumstatik und Baumkontrolle. Patzer-Verlag, Berlin,1998.
Wikipedia. Weather stations. http://en.wikipedia.org/wiki/Barometer, October 2010a.
Wikipedia. Weather stations. http://en.wikipedia.org/wiki/Disdrometer, October 2010b.
Wikipedia. Weather stations. http://en.wikipedia.org/wiki/Hygrometer, October 2010c.
Wikipedia. Air pollution. http://en.wikipedia.org/wiki/Air_pollution, October 2010d.
Wikipedia. Urban climate. http://en.wikipedia.org/wiki/Urban_climate, October 2010e.
Wikipedia. Cluster analysis, September 2010f. http://en.wikipedia.org/wiki/clusteranalysis.
Qiming Zhou. Relief shading using digital elevation models. Computers and Geo-sciences, 18(8):1035 – 1045, 1992. ISSN 0098-3004. doi: DOI:10.1016/0098-3004(92)90019-N. URL http://www.sciencedirect.com/science/article/B6V7D-48CFVD7-92/2/
0da0438e2494abd1e5fb47e8ce441642.
Sisi Zlatanova. 3D exchange formats, 2010. Lecture slides of 3D GIS course.
71
72
Appendix A
Sensing
A.1 Positioning combinations
• GPS and INS combination The combination of GPS and INS is considered to provideposition tracking outdoors, in blind spots and indoors. However, this combination is onlyreliable and accurate for short periods of time and that is because the errors presentedhave accumulation characteristics i.e. the errors of previous positions will be present forthe next determined position. This combination method needs some extra equipment todevelop and therefore the structure of the current devices has to be changed, as a matterof ubiquitousness.
• GPS and GSM combination GSM positioning uses large cell towers to connect mobiledevices. The method is based on the capability of the network to estimate the position ofa cell phone by identifying the cell tower that the device is using at a specific time [Le et.Al, 2009]. Continuous tracking of position would be very dependent on the density of thecell towers and the distance to the devices (e.g. cell phones). The accuracy of the systemwould be very low.
• GPS and Infrared combination Infrared (IR) wireless networking was a pioneer tech-nology in the field of indoor positioning [Bahl et al., 2000]. An early implementationof an IR technique is the Active Badge System. This is a remote positioning system inwhich the location of a person is determined from the unique IR signal emitted every tenseconds by a badge he is wearing [Le et. Al, 2009 ]. The accuracy of the system is high,however the system suffers from several limitations such as limited range of IR, sensitivityto sunlight, etc. as well as high cost of implementation.
• GPS and Wi-Fi combination Wi-Fi is an attractive candidate among other positioningcombinations due to its network, i.e. densely deployed Wi-Fi access points (APs), and itsubiquitous hardware, i.e Wi-Fi enabled mobile devices. Wi-Fi APs can be found almosteverywhere providing a good coverage in both outdoor and indoor spaces. Therefore Wi-Fi could make up for the shortcomings of GPS and especially for the blind spots. Theimplementation costs of a Wi-Fi positioning system would be low if the network is usableas it is. The accuracy depends on the density and distribution of APs. When this isoptimal, 3-5 meters accuracy can be acquired (according to Ekahau RTLS specifications).
The Wi-Fi network strength is considered to vary due to multipath propagation; however,depending on the positioning method used, a Wi-Fi positioning system may not requirethe mobile user to have a line-of-sight path to the Wi-Fi AP and therefore could be leastaffected by multipath.
• GPS and Bluetooth combination Bluetooth is similar to WiFi communication butwith limited range and communication speed (10m for Class 2 over 3Mb/s Le (2009)).
73
Bluetooth communication is mainly used by cell phones and notebooks. The weakestpoints are the limited ranges and lack of central station for providing communicationservices. Currently there is no Bluetooth communication within the campus. The imple-mentation cost would be high, the time is limited and the results would not be robust.
A.2 Blind spots
Depending on the local visibility, an elevation mask as a function of azimuth is set, to filterout the invisible satellites with respect to their elevation and azimuth. The default elevationcut-off angle is zero; that is, equal to horizon. In the end, the results will be added up duringthe specified time span. The algorithm that can can result in the definition satellite availabilityspots is given in Figure A.1.
Figure A.1: The algorithm for satellite availability spots definition
The elevation mask can be computed from 3D virtual world model if the position of the receiveris known. The line-of-sight(LOS) vectors would be calculated by subtracting the user positionvector in ECEF frame from each of the satellite positions for all times of interests however atthe end vectors and 3D model should in the same coordinate frame.
Below elevation and azimuth angles for the 24 GPS and the 27 Galileo satellites have beencalculated for each minute during a full daytime for the test area in TUDelft, the test observerpoints have chosen with height of 1.8 m above the street level within 10 meters distance ofbuildings. The actual visibility calculation is performed within the GIS package ArcView 3.2awith the extension 3D Analyst. The simulation of the availability of GNSS consists of twoalgorithms. The first one calculates the total of targets (satellite positions at a certain time)seen from the observation point by:
Count = 0
for each aTarget ’possible observable satellite
74
if (aTIN.LineOfSightsAsShapes (anObserver, aTarget,
ListOfShapes) = True) then
Count = Count + 1
end
end
The second algorithm calculates the availability of ’enough’ satellites during a day time. Eachtarget (24 GPS satellites and 27 Galileo satellites) within these constellations is checked by therequest:
aTIN.ObscuresTarget (anObserver, aTarget)
For GPS and Galileo alone observing, four satellites simultaneously is the minimum for a positionfix . Availability is analysed here regardless the actual geometry of the visible satellites, whichcan have a large impact on the eventual position accuracy Verbree and Zlatanova (2007).
A.3 Python combination algorithm
The basic script of the system is written in Python. The steps followed to finally obtain aposition of the client are:
• The GPS is connected to one of the client’s communication ports.
• The system receives the GGA message from the GPS receiver. The condition is esti-mated and the position is extracted for the message- when possible, as explained in theCombination Algorithm section.
• The Wi-Fi tablet transmits the signal strength of the APs to the server and the systemasks from the server to give the position estimate.
• Through the combination algorithm the estimated GPS and Wi-Fi positions are comparedand the better is chosen
• The system applies the coordinate transformation on the position defined as more accu-rate.
++++++++++++++++Initialising the packages that will be used++++++++++++++++
mport ekahau,GPS,temperature,posdecision,time,thread
user = ’Geomatters’
password = ’geomatters’
MAC = ’00:13:CE:B6:54:39’
queryurl = ’http://131.180.169.140:8550/epe/pos/tagstream?mac=’ + MAC
wifi_initialised = False
gps_initialised = False
sensor_initialised = False
def polTag(tag):
global wifi_initialised
while not wifi_initialised:
if hasattr(tag, ’time’):
wifi_initialised = True
else:
print ’Wifi has no initial fix...’
time.sleep(5)
def polGPS(gps):
global gps_initialised
while not gps_initialised:
if hasattr(gps, ’timeOfFix’):
75
gps_initialised = True
else:
print ’GPS has no initial fix...’
time.sleep(5)
def polSensor(sensor):
global sensor_initialised
while not sensor_initialised:
if hasattr(sensor, ’time’):
sensor_initialised = True
else:
print ’Sensor has no initial measurement...’
time.sleep(5)
def threadexittest():
for i in range(5):
time.sleep(1)
print i
print ’Thread count = ’,thread._count()
thread.interrupt_main()
tag = ekahau.Tag()
gps = GPS.GPS()
sensor = temperature.TemperatureSensor()
decision = posdecision.Decision()
thread.start_new_thread(ekahau.TagStreamListener,(user,password,queryurl,tag))
thread.start_new_thread(gps.GGA,())
thread.start_new_thread(sensor.read,())
thread.start_new_thread(polTag, (tag,))
thread.start_new_thread(polGPS, (gps,))
thread.start_new_thread(polSensor, (sensor,))
time.sleep(10)
thread.start_new_thread(decision.start, (gps, tag, sensor))
#thread.start_new_thread(threadexittest, ())
try:
while 1:
pass
except:
print "Thread exited...."
print "System is closing..."
thread.exit()
++++++++++++++++++Inserting the Ekahau software and its measurements++++++++++
import pycurl,thread,coordtransformation
from xml.etree.ElementTree import XML
class Tag:
def __init__(self):
self.lastposfailed = True
self.accuracy = 33 #meters accuracy 90% from the accuracy statistics of Ekahau
def setAll(self,tagid,MAC,position,quality,mapid,mapname,date,time):
self.tagid = tagid
self.MAC = MAC
self.position = position
self.quality = quality
self.mapid = mapid
self.mapname = mapname
76
self.date = date
self.time = time
def FromMAC(self, tree, MAC):
try:
for tag in tree.findall("TAG"):
if tag.findtext(’mac’) == MAC:
self.tagid = getTagIDFromMAC(tag)
self.MAC = MAC
self.position = getTagPositionFromMAC(tag)
self.latitude, self.longitude = getTagLatLonFromMAC(tag)
self.quality = getTagQualityFromMAC(tag)
self.mapid = getTagMapIDFromMAC(tag)
self.mapname = getTagMapNameFromMAC(tag)
self.date = getTagDateFromMAC(tag)
self.time = getTagTimeFromMAC(tag)
return self
except:
print ’Tag with MAC: ’,MAC,’ not found.’
class TagStreamListener:
def __init__(self,User,Password,Stream_Url,tag):
self.tag = tag
try:
if Stream_Url.find(’mac=’) is -1:
raise
except:
thread.interrupt_main()
self.MAC = Stream_Url[Stream_Url.find(’mac=’)+4:Stream_Url.find(’mac=’)+21]
conn = pycurl.Curl()
conn.setopt(pycurl.USERPWD, "%s:%s" % (User, Password))
conn.setopt(pycurl.HTTPAUTH, pycurl.HTTPAUTH_ANY)
conn.setopt(pycurl.URL, Stream_Url)
conn.setopt(pycurl.WRITEFUNCTION, self.on_receive)
try:
conn.perform()
except:
print ’Could not connect to Ekahau Positioning Engine’
thread.interrupt_main()
def on_receive(self,data):
if (data[11:16] == ’<TAG>’):
self.tag.lastposfailed = False
responsetree = XML(data)
self.tag = self.tag.FromMAC(responsetree, self.MAC)
def getTagIDFromMAC(tag):
tagid = tag.findtext(’tagid’)
if tagid is not None:
return tagid
return ’Tag ID unknown...’
def getTagLatLonFromMAC(tag):
posx = tag.findtext(’posx’)
posy = tag.findtext(’posy’)
#return tag position [x,y]
if posx and posy is not None:
try:
xrd, yrd = coordtransformation.ekahauRDTransformation(int(posx),int(posy))
return (xrd, yrd)
except:
77
print ’Position transformation failed... posx: ’,posx, ’posy: ’,posy
return ’Tag position unknown...’
def getTagPositionFromMAC(tag):
posx = tag.findtext(’posx’)
posy = tag.findtext(’posy’)
#return tag position [x,y]
if posx and posy is not None:
return (posx,posy)
return ’Tag position unknown...’
def getTagQualityFromMAC(tag):
quality = tag.findtext(’posquality’)
if quality is not None:
return quality
return ’Tag quality unknown...’
def getTagMapIDFromMAC(tag):
mapid = tag.findtext(’posmapid’)
if mapid is not None:
return mapid
return ’Tag Map ID unknown...’
def getTagMapNameFromMAC(tag):
mapname = tag.findtext(’posmapname’)
if mapname is not None:
return mapname
return ’Tag Map Name unknown...’
def getTagDateFromMAC(tag):
datetime = tag.findtext(’postimestamp’)
if datetime is not None:
datesplitted = datetime.split(’ ’)[0].split(’-’)
return datesplitted[0]+’/’+datesplitted[1]+’/’+datesplitted[2]
return ’Tag Date unknown...’
def getTagTimeFromMAC(tag):
datetime = tag.findtext(’postimestamp’)
if datetime is not None:
return datetime.split(’ ’)[1].split(’+’)[0]
return ’Tag Time unknown...’
++++++++++++++++++Inserting GPS software and its measurements++++++++++++++++++
import serial,thread,coordtransformation
class GPS:
def __init__(self):
self.connect()
def connect(self):
self.connection = serial.Serial()
available = []
for i in range(10):
try:
self.connection = serial.Serial(i)
available.append(self.connection.portstr)
self.connection.close()
except serial.SerialException:
pass
for port in available:
self.connection = serial.Serial(port,9600)
78
if self.connection.readline()[16:22] == ’u-blox’:
self.recievertype = ’U-blox’
print ’U-blox connected on’,port
break
else:
self.connection.close()
try:
if not self.connection.isOpen():
raise
except:
print ’U-Blox not found...’
thread.interrupt_main()
def GGA(self):
while (1):
if self.connection.isOpen():
msg = self.connection.readline()
if msg[3:6]==’GGA’:
self.parseGGA(msg)
else:
print ’COM Reconnecting...’
self.connect()
def parseGGA(self,sentance):
(self.format,
self.utc,
self.latitude,
self.northsouth,
self.longitude,
self.eastwest,
self.gpsfix,
self.number_of_satellites_in_use,
self.horizontal_dilution,
self.altitude,
self.altitude_unit,
self.geoidal_separation,
self.geoidal_separation_unit,
self.data_age,
self.diff_ref_stationID) = sentance.split(",")
if len(sentance)<55:
self.gpsfix = 0
else:
self.gpsfix = int(self.gpsfix)
self.horizontal_dilution = float(self.horizontal_dilution)
latitude_in=float(self.latitude)
longitude_in=float(self.longitude)
#if self.northsouth == ’S’:
# latitude_in = -latitude_in
#if self.eastwest == ’W’:
# longitude_in = -longitude_in
latitude_degrees = int(latitude_in/100)
latitude_minutes = latitude_in - latitude_degrees*100
longitude_degrees = int(longitude_in/100)
longitude_minutes = longitude_in - longitude_degrees*100
79
latitude_out = latitude_degrees + (latitude_minutes/60)
longitude_out = longitude_degrees + (longitude_minutes/60)
XY = coordtransformation.wgs2rd(latitude_out, longitude_out)
self.latitude = XY[0]
self.longitude = XY[1]
self.timeOfFix = str(int(self.utc[0:2])+2)+’:’+self.utc[2:4]+’:’+self.utc[4:6]
#self.timeOfFix = time.strftime("%H/%M/%S", time.strptime(self.utc.split(".")[0],"%H%M%S"))
self.altitude = float(self.altitude)
++++++++++++++++++++++Defining the positioning technique++++++++++++++++++
import time
class Decision:
def __init__(self):
filename = ’Measurements_’+time.strftime("%Y%m%d%H%M%S")+’.txt’
self.file = open(filename,’w’)
def start(self,gps,wifi,sensor):
while(1):
#### Position Decision ####
result1,result2 = posdec(gps,wifi)
if result1 == "GPS":
gpsprint(gps,result2)
sensorprint(sensor)
writetofile(self.file,gps.latitude,gps.longitude,gps.altitude,result2,sensor.temperature,gps.timeOfFix)
elif result1 == "WiFi":
wifiprint(wifi,result2)
sensorprint(sensor)
writetofile(self.file,wifi.latitude,wifi.longitude,0,result2,sensor.temperature,wifi.time)
elif result1 == "No position":
print result2
wifi.lastposfailed = True
time.sleep(10)
def posdec(gps,wifi):
if gps.gpsfix == 0:
if wifi.lastposfailed:
return "No position", "No position estimation"
else:
wifi_acc = wifiacc(wifi)
return "WiFi", wifi_acc
else:
GPS_acc = GPSacc(gps,gpsfixcheck(gps))
wifi_acc = wifiacc(wifi)
if wifi.lastposfailed:
return "GPS",GPS_acc
else:
if GPS_acc <= wifi_acc:
return "GPS",GPS_acc
else:
return "WiFi",wifi_acc
def gpsprint(gps, GPS_acc):
print gps.timeOfFix, ", Lat:", gps.latitude,gps.northsouth, ", Lon:", gps.longitude,gps.eastwest, " , Alt:",gps.altitude
print "GPS_Fix:",gps.gpsfix,", Sats in view:",gps.number_of_satellites_in_use,", HDOP:", gps.horizontal_dilution
80
print "GPS accuracy of: ", GPS_acc, "meters (95%)"
def gpsfixcheck(gps):
rec_acc1,rec_acc2 = Rectypecheck(gps)
if gps.gpsfix == 1:
return rec_acc1
elif gps.gpsfix == 2:
return rec_acc2
else:
return 0
def Rectypecheck(gps):
if gps.recievertype == ’U-blox’:
return 6,4.8 #first is gps, seccond is Dgps
elif gps.recievertype == "Garmin GPSMAP 76CSx":
return 10,5 #first is gps, seccond is Dgps
def GPSacc(gps,rec_acc):
if rec_acc is not 0:
return gps.horizontal_dilution * rec_acc
else:
return ’Unknown’
def wifiprint(wifi,wifi_acc):
print wifi.time, ", Lat:", wifi.latitude, ", Lon:", wifi.longitude, ", Alt:",1.5
print "WiFi_Lastposfailed:",wifi.lastposfailed,", Quality:", wifi.quality
print "WiFi accuracy of: ", wifi_acc, "meters (90%)"
def wifiacc(wifi):
if wifi.accuracy is not None:
return (1-(float(wifi.quality)/100))*wifi.accuracy
else:
return ’Unknown’
def sensorprint(sensor):
print ’Temperature:’,sensor.temperature,’degrees Celsius’
def writetofile(file,x,y,z,sigma,temperature,timestamp):
#format: 13,13,15,2.2,17.1,21/09/10 21:23:02
line = str(x)+’,’+str(y)+’,’+str(z)+’,’+str(sigma)+’,’+str(temperature)+’,’+time.strftime("%d/%m/%y")+’ ’+timestamp
file.write(line+ ’\n’)
file.flush()
A.4 Measurements
A.4.1 Tablet PC
• Intel Pentium M (Centrino) processor 1.50GHz, 1MB L2 cache
• 512MB DDR SDRAM memory
• 40GB 4200 RPM ATA/100 hard drive
• 14.1” XGA (1024 x 768) TFT LCD screen
• PMCIA Wi-Fi card: AirLancer MC-54AG: IEEE 802.11a/b/g Wireless LAN adapter(supporting up to 108 Mbps)
81
A.4.2 Arduino board and temperature sensors
Temperature sensor- TMP36GT9Z The TMP36 is a low voltage, precision centigradetemperature sensor that provides a voltage output nearly proportional to the Celsius (centi-grade) temperature. It provides typical accuracies of +/-2oC over the -40oC to 125oC and+/-1oC at +25oC temperature range and therefore no external calibration is needed. TheTMP36 is intended for simple supply-operation from 2.7V to 5.5V maximum and its supplycurrent runs well below 50?A, providing very low self-heating, less than 0.1oC in still air. Inaddition, a shutdown function is provided to cut the supply current to less than 0.5 A. The out-put scale factor of the sensor is 10mV/ oC. The sensor has to be properly mounted (cementedor glued) on the surface of the medium and then the difference to the air temperature will bewithin 0.01oC. During the implementation that inaccuracy is not taken into account.
The specifications of TMP36GT9Z are given below in a summary.
• Low voltage operation at 2.7 V to 5.5 V
• Direct calibration in Celsius degrees
• 10 mV/oC scale factor
• +/-2oC accuracy over temperature (Typical)
• +/-0.5 oC linearity (Typical)
• Stable with large capacitive loads
• Specified at -40 oC to +125 oC
• Less than 50 ?A quiescent current
• Shutdown current 0.5 ?A maximum
• Low self-heating
Arduino board Arduino can sense the environment by receiving input from a variety ofsensors and can affect surroundings by controlling lights, motors and other actuators. TheArduino module is a simplified microcontroller board. Although there are an increasing numberof alternate form factors, the original design includes all the electronic parts necessary to powerand communicate with the microcontroller: regulator, clock crystal, USB-to-serial interface,and SPI programming interface for replacing the boot-loader. The boards can be built by handor purchased preassembled.
In summary Arduino specifications are given in the table below.
• ATmega168 microcontroller
• Operating voltage at 5V
• (Recommended) input voltage at 7-12V
• Input voltage limits at 6-20V
• 14 digital I/O pins (6 of them provide PWM output)
• 6 analog inputs
• 40mA DC current per I/O pin
• 50mA DC current for 3.3V pin
• Flash memory of 16 KB (ATmega168) or 32 KB (ATmega328) of which 2 KB used byboot-loader
82
• SRAM at 1 KB (ATmega168) or 2 KB (ATmega328)
• EEPROM at 512 bytes (ATmega168) or 1 KB (ATmega328)
• 16MHz clock speed
Arduino board software Arduino board is used as the medium to transfer the temperaturemeasurements from the sensor to the computer. An Arduino is not just a hardware piecebut also the software that lets you program and communicate with the board. The Arduinomicrocontroller is programmed using the Arduino programming language (based on Wiring)and the Arduino development environment (based on Processing). Arduino software can bedownloaded for free. Running the software with the code provided in Arduino language givesthe recordings of the temperature sensor. The Arduino language is based on C/C++ whereasthe source code for used for communication with the board is also available in Java.
A.4.3 Server
• Intel(R) Core(TM)2 Duo CPU E6850 @ 3.00GHz (2 CPUs) processor
• 3568MB RAM memory
• Windows XP Professional (5.1, Build 2600) Service Pack 3 operating system
• NVIDIA GeForce 8400 GS display
A.5 Integration Python code
# Very simple serial terminal
# Effrosyni Boufidou
import serial,time,thread
class TemperatureSensor:
def __init__(self):
self.connect()
def connect(self):
self.connection = serial.Serial()
available = []
for i in range(10):
try:
self.connection = serial.Serial(i)
available.append(self.connection.portstr)
self.connection.close()
except serial.SerialException:
pass
for port in available:
self.connection = serial.Serial(port,9600)
if self.connection.readline()[6:13] == "degrees":
print ’Arduino connected on’,port
break
else:
self.connection.close()
try:
if not self.connection.isOpen():
raise
except:
print ’Arduino not found...’
83
thread.interrupt_main()
def read(self):
while (1):
self.connection.write("r d\r")
o = self.connection.readline()
if len(o) == 0:
print "no response..."
else:
if o[6:13] == "degrees":
self.temperature = o.split(" ")[0]
self.time = time.strftime("%H:%M:%S")
A.6 Indoor positioning systems
A.6.1 Indoor MEssaging System (IMES)
In order to solve the availability issue, the Japan Aerospace Exploration Agency (JAXA),the GNSS Technologies, and the Lighthouse Technology and Consulting have developed anIndoor MEssaging System(IMES) to solve the availability issue in GPS indoors. Composed oftransmitters, GPS receivers with modified firmware-embedded in cell phones-, and systems ofservers, IMES aims to provide seamless positioning anywhere in a covered area.
It uses satellite signals outdoors while using signals from IMES transmitters indoors, wheresatellite signal quality is strongly reduced. The IMES signal structure is similar to that of GPSsatellite signals, except for the contents of the navigation message. Thus, the same receivercan be used for both. An IMES transmitter sends a Radio Frequency (RF) signal, similar tothat of GPS and the Japanese Quasi-Zenith Satellite System (QZSS). This signal provides thetransmitter’s 3-D position,i.e. the position of the centre of its cell coverage zone. Moreover, itlinks the receiver to a database corresponding to an identifier gpsworld.com (a).
Some of the limitations of IMES:
• Infrastructure: a massive investment in infrastructure is needed. The transmitters needto be very densely located, i.e. at separations of 20- 30 meters, in all indoor spaces.
• Jamming: IMES affect a small area around the transmitter; however within this areaIMES completely jam GPS
• Security: thousands of transmitters are needed for an IMES to work properly in public ar-eas. These transmitters are easily installed however easily removed too. A lost transmitterin such a system can cause havoc and a non-functional system.
• Frequency allocation and regulation: IMES only operate in countries where the IMESsignal has been sanctioned in the L1 band.
A.6.2 Pseudolites
A Pseudolite is a signal generator that transmits GPS-like signals to nearby users. It uses analgorithm that can calculate the phase centre of each Pseudolite antenna with millimeter-levelaccuracy. The reference station is fixed at the floor and transmits carrier phase corrections tothe user by means of a wireless datalink. Applying these corrections, the user calculates itsposition using carrier phase differential GPS (CDGPS). This approach is fundamentally similarto outdoor CDGPS. However, an indoor navigation system is more difficult to implement dueto a near/far problem i.e. the received power from nearby transmitters is stronger than this
84
from the distant ones and acts as an interfering source. Furthermore, multipath has to be takeninto account.
Another important aspect is the position of the transmission antennas. These must be mea-sured very accurately since a small error in the transmitter’s position creates a relativelylarge line-of-sight vector error due to the very short distances between users and Pseudolitesg-psworld.com/wireless.
Pseudolites are able to transmit GPS-like signals or other ranging signals at different frequencies.Both types of Pseudolites can supplement GNSS by providing extra ranging signals and animproved RF transmitter geometry and hence make precise positioning possible in restrictedareas or even indoors.
Typically, a minimum of four Pseudolite transmitters with known positions are needed to ob-tain unambiguous positions and time estimation. Accurate time synchronization between thePseudolite transmitters is also important gpsworld.com (b).
A.7 GPS mounted on an Arduino board
The Arduino board is able to provide geo-located sensor measurements as well as to log thesesensor data along with their precise time and location . The GPS shield supports four differentkinds of GPS modules and stores data on a standard DOS-formatted SD flash memory card.Latter, the data captured can be exported in a plain text file or in a spreadsheet and canbe latter used by the 3D model. The GPS shield comes in pieces and the board has to beassembled and connected to the Arduino board in a bridge structure. Latter, the sensor thatwas simply connected to the Arduino board in one of the analog inputs passes through the GPSshield too. Both the sensor data and the NMEA positioning message broadcast from the samecommunication port and the the result is a positioned sensor measurement. This sensor box caneither work with a battery or plugged in a computer with a USB cable.Figure A.2 illustratesthe GPS and Arduino boards set-up together with a low voltage TMP36 temperature sensor.
The GPS module used was a USGlobalSat EM-406A. This module provides an accuracy of 5-10 meters and is intended for positioning outdoors. Its output is in standard NMEA format.This GPS mounted on the Arduino, uses again the Arduino microcontroler’s software and thescript for position acquisition is in Wiring. A Python script has to be implemented in order toget the GPS measurements directly and not through the Arduino software. That would offercompatibility with the other parts. Some positioning results are given in Figure A.3.
When the sensor is also mounted and in the same communication port as the GPS, the resultcomes with the sensor measurement integrated. Unfortunately that was the step that was notimplemented during the limited time of the project. A Python script had to be implementedin order to get the measurements from the GPS and the sensor from the same communicationport.
This sensor box would give a very good solution for dynamic climate parameter measurements.It is about a lightweight box that can be carried without the need for current since it workswith a battery and with the ability to store directly the measurements. Its functionality has tobe therefore further investigated.
85
Figure A.2: GPS shield mounted on an Arduino board.
Figure A.3: The output of the GPS module when mounted on an Arduino board. The format ofthe output is in standard NMEA and the message is a GPGGA message with all the informationabout time, location, accuracy, e.t.c.
86
Appendix B
3D modelling
B.1 CityGML
B.1.1 Vegetation Object Model
Figure B.1: Involved Vegetation model database tables.
Figure B.1 shows the database tables involved in the storage of one tree. In total, five ta-bles are involved, the superior class table is called CITYOBJECT, it has ID, CLASS ID andCREATE DATE, ID must be unique for each tree, all trees have a CLASS ID value 7, whichrepresent the object type. Then comes the table¡SOLITARY VEGETAT OBJECT¿, in whichhas column: ID, NAME, SPECIES, HEIGHT, and LODx GEOMETRY ID (x=1, 2, 3, 4),the value of ID must be as the same as in CITYOBJECT, LODx GEOMETRY ID are for-eign key point to table SURFACE GEOMETRY. Then, in table SURFACE GEOMETRY, an’object container’ must be defined using the same id as it used in table CITYOBJECT, inthis case, it has value: 1. After that, LODx Geometry identifier must be defined as well, theLODx GEOMETRY ID in table SOLITARY VEGETAT OBJ are foreign keys point to theseidentifiers in table SURFACE GEOMETRY, which has ID value 2,3,4,5 in this case. Then,the real geometry comes in, each face are stored as one record, which start from id: 6 till the
87
end, the PARENT ID and ROOT ID are used to identify the faces of the different LODs andTREEs. Fig3 is a good example to get this idea.
After the SURFACE GEOMETRY are filled, the following table SOLITARY ADDITIONAL PARAused to store the tree-based parameters, like: NDVI, etc; table VEGETAT DICTIONARYstores species-based parameters, like: PAI, WAI, Drag coefficient, etc.
88
B.1.2 Landuse model
The related tables are CityObject, Landuse and Surface Geometry. CityObject is a top leveltable, every feature modeled is registered as one tuple in this table. The land cover type is storedin table Landuse (Name), and geometry in table Surface Geometry. Each feature in Landusehas an ID as primary key, which points to an ID in CityObject; while a LODX Gemetry ID(in our case, 2D polygon, X=0) points to an ID of some feature in Surface Geometry. Inthe Surface Geometry, those records with IDs that exist already in LODX Multi Surface IDof Landuse are abstract featuer and have null in Geometry. In the example, Faculty LR hasgeometry in the LOD0, which points to a record ID = 7008 in the Surface Geometry. Thisrecord doesnt store geometrical info, but some other record with ID = k (K can be any otherunique number) store geometrical info, which is indicated by its Parent ID = 7008 and Root ID= 7008. Likewise, other land cover features like Geosciences and Grassland have geometry withID = 7014 and 7027, respectively. And records in Surface Geometry with Parent ID = 7014 andRoot ID = 7014, and with Parent ID = 7027 and Root ID = 7027 store the actual geometricalinfo for Geosciences and Grassland, respectively.
Figure B.2: Involved Land Use model database tables.
B.1.3 Generic CityObject
Measurements like Position and Temperature are taken simultaneously from GPS/WIFI andsensors. For each measurement, an position information (x,y,z), accuracy, temperature (◦),and measured-time (dd/mm/yyyy, HH:MM:SS) are obtained. Figure B.3 shows the first tenmeasurements during 2 minutes.
To store five measurements, four tables are involved (figure B.4). First, five records must bestored in table¡CITYOBJECT¿ with CLASS ID value 5. Then, a table called GENERIC CITYOBJECTis needed to be filled with the same id value as it in CITYOBJECT, moreover, LOD1 GEOMETRY IDmust be different with ID value, which are foreign keys point to table SURFACE GEOMETRYlater on, in this case are value 6,7,8,9,10. After that, SURFACE GEOMETRY are used to store
89
Figure B.3: Data format of the measurements as received from the tracking/measuring devices
Position information(X, Y, Z coordinate in RD system), however, ’containers’ must be definedusing the same ID again as them in CITYOBJECT, in this case are 1,2,3,4,5. Then comesLODx GEOMETRY identifiers, in this case identifiers are 6, 7, 8,9,10, which are all of LOD1.Parent id and Root id are the same here, only used to tell which measurement this record is.
Figure B.4: Tables involved when storing measurements.
90
B.1.4 DTM storage
DTM in CityGML is treated as a superclass has 4 different kinds of representation, mass point,break line, grid and TIN. We choose TIN (a subclass of DTM) approach. Storing TIN refers to6 tables, CityObject, Relief Feature, Relief Component, Relief Feat To Rel Comp, TIN Reliefand Surface Geometry. Like all the CityGML objects, table CityObject registers the TIN witha unique ID, with Class ID = 16. For TIN is a subclass of DTM, whenever there is a record ofTIN, there must be also a corresponding record indicating DTM in CityObject with a uniqueID and Class ID = 14. As shown in the example, a DTM as relief feature has an objectwith ID = 17 in CityObject. And the terrain representation TIN has an object with ID = 39.Table Relief Feature stores the DTM object, which has the same ID (= 17) as in the CityObject.Table Relief Component stores the representation of DTM (in our case is TIN) and has a recordwith the same ID (= 39) as in CityObject. The internal relation between Relief Feature andRelief Component is stored in Relief Feat To Rel Comp, which has the Relief Component IDand Relief Feature ID pointing to the two tables. In the TIN Relief, a TIN object with thesame ID as in Relief Component and CityObject has a Surface Geometry ID, which points tosome record in table Surface Geometry. As all the other CityGML object, this record doesntstore geometry itself and has Parent ID and Geometry equal to null. As TIN is a triangulatedsurface, this TIN record has the attribute IS Triangulated = 1. Its actual geometry is stored inother records, which have Parent ID and Root ID equal to its ID (in our case ID = 288). Eachgeometrical record represents a triangle and stores coordinates of 3 vertices. So the fact thatn rows of geometrical record shows that this TIN object has n triangles. Figure B.5 shows thetables and their relationships.
Figure B.5: Tables involved when storing a DTM in CityGML
91
B.2 Tree parameters deriviation
To estimate the Leaf Area Index (LAI), knowledge about the acquisition system should be athand. As these specifications are of importance for an understanding of the AHN2 product. TheAHN2 product was acquired by the flimap system from Fugro. The flimap system is equippedwith two laser scanners, both with a deviation of 7◦ from the normal, the swath range is 60◦.If an assumption about the flight height and speed is taken, the coverage of the laser beam canbe calculated. This is shown in figure B.6.
115.5(= 100/cos30◦)
0.072(= tan0.036◦ × 115.5)
front view side view
0.036◦(= 60◦/1667)
pulse frequency: 250.000 [Hz]
scan frequency: 150 [Hz]
Flyingspeed = 50 [km/h] or 13.9 [m/s]
0.093(= 13.9/150)
ground view
Beam divergence = 0.45 [mrad]
0.093
0.052
0.072
7◦
Figure B.6: The flimap system, resolution, beam angle and footprint.
These separation distances give a theoretical ground coverage of 149 points per m2 = (1/(0.072×0.093)). If this number is multiplied by the footprint of the beam (Af = 0.0021m2(= (0.052/2)2×π)), the covered area is calculated (Alasercoverage = 0.31715m2/m2). This sampling area is rela-tively dense, thus if the ground covered area (At) is calculated through the laser data, this willgive an estimate of the real situation. This calculation is done by using the same convexhullalgorithm. With all these parameters it is possible to estimate the Wood Area Index, by:
WAI =N/2×Af ×A−1
lasercoverage
Atree(B.1)
In this formula the number of laser measurements (N) is divided by 2, as a tree is sampled atleast twice by the FLIMAP lasers. Each laser measurement is multiplied by its coverage (Af ),and the sampling coverage.
If this formula is applied to the dataset the estimates are in the order of 0.01. This is a factorof 100 too low. We attempted to estimate WAI and LAI direclty from the laser points, but thisrequires additional research.
With the estimate of the wood area index it is possible to extract for each species the LAI, bythe use of the ratio of WAI:PAI (wp);
LAI =1− wpwp
×WAI (B.2)
92
Plant Area Index = Woody Area Index + Leaf Area Index
Figure B.7: Relation between wood area index, leaf area index and plant area index.
93
B.3 Terrain processing
Following is a detailed description of the terrain reconstruction process.
B.3.1 Selecting parts of the AHN2 dataset
1. Merge g11 and g16 with asmerge.exe
2. Select area of OTB, Mekelpark, TU Campus using las2las.exe
Following is the Python code for step 3.
B.3.2 Create TIN
Triangulate elevation points and store points in ASCII las2tin.exe
B.3.3 Convert TIN to suitable CityGML format
Convert points to coordinates with GM2100 TINprocessing.py :
# TIN Processing
# GM2100 synthesis project 2010
def TIN_processing(input_lidar,input_tin,output_lidar):
""" function to match TIN points with RD coordinates """
print ’Running TIN processing\n’
lidar = input_lidar
data = open(lidar, ’r’)
print ’Reading LIDAR from file <’ + str(lidar) + ’>’
tin = input_tin
points = open(tin, ’r’)
print ’Reading TIN points from file <’ + str(tin) + ’>’
tin_output = output_lidar
output = open(tin_output, ’w’)
print ’Match all TIN points with x,y,z-coordinates’
print ’\nPLEASE WAIT...\n’
# create lidar dictionary with ID number for points
lidar_dictionary = {}
idnum = 0
while 1:
line_lidar = data.readline()
if not line_lidar: break
information = line_lidar.split()
lidar_dictionary[idnum] = information
idnum = idnum+1
94
# iterate traingles
while 1:
line_points = points.readline()
if not line_points: break
triangle = line_points.split()
coords_line = []
for point in triangle:
coordinates = lidar_dictionary[int(point)]
for coords in coordinates:
coords_line.append(coords)
# prepare output
out_line = ’,’.join(coords_line)
output.write(out_line)
output.write(’\n’)
points.close()
data.close()
output.close()
return ’Completed! Output in file <’ + str(tin_output) + ’>’
print TIN_processing(’lidar_OTB.txt’,’tin_OTB.txt’,’DTM_OTB.txt’)
95