modernizing geodesy education in western balkan with...

Modernizing geodesy education in Western Balkan with focus on

competences and learning outcomes - GEOWEB

DRAFT VERSION March 2017

2015-2018

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Contents Draft Curriculum ........................................................................................................................ 3

Reference Systems in Space and Time ...................................................................................... 4

Precise Positioning and Navigation ........................................................................................... 5

Physical geodesy ........................................................................................................................ 6

Laser Scanning ........................................................................................................................... 7

Geovisualization ........................................................................................................................ 9

Web-GIS .................................................................................................................................. 10

Advanced Programming .......................................................................................................... 11

Project management ................................................................................................................. 13

Research methodology and communication ............................................................................ 14

Geodynamics and deformation analysis .................................................................................. 16

Advanced theory of adjustment ............................................................................................... 17

Geodetic optimization .............................................................................................................. 18

Integrated sensor technologies ................................................................................................. 19

Precise industrial measurements .............................................................................................. 21

Geodetic Space Techniques ..................................................................................................... 22

Spatial databases and SDI ........................................................................................................ 23

Spatial Analysis ....................................................................................................................... 24

Geostatistics ............................................................................................................................. 26

Location-Based Services .......................................................................................................... 27

GIS in spatial planning............................................................................................................. 28

Digital Photogrammetry........................................................................................................... 29

Advanced Remote Sensing ...................................................................................................... 30

Applied Mathematics ............................................................................................................... 32

Real estate and investment analysis ......................................................................................... 33

Land Consolidation .................................................................................................................. 34

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Draft Curriculum

Sem: 1 Year: 1

Sem: 2 Year: 1

No. Course Name Course ETCS No. Course Name Course ETCS

1 Physical geodesy C 5 1 Reference systems in space and time C 5

2 Laser Scanning C 5 2 Geovisualization C 5 3 Advanced programming C 5 3 Web-GIS C 5

4 Advanced theory of adjustment S 5 4

Integrated sensor technologies S 5

5 Geodetic optimization S 5 5 Geodynamics and deformation analysis S 5

6 Spatial databases and SDI S 5 6 Precise industrial measurements S 5

7 Applied Mathematics S 5 7 Spatial Analysis S 5

8 Real estate and investment analysis S 5 8

Digital Photogrammetry S 5

9 Land Consolidation S 5 Total=

45

Total= 40

MSc C = 15 MSc C = 15 MSc S = 30 MSc S = 25 Sem:

3 Year: 2 Sem: 4 Year: 2

No. Course Name Course ETCS No. Course Name Course ETCS

1 Precise positioning and navigation C 5 1 Diploma project C 30

2 Project management C 5

3 Research methodology and communication C 5

4 Geodetic space techniques S 5

5 Geostatistics S 5 6 Location-Based Services S 5 7 GIS in spatial planning S 5

8 Advanced Remote Sensing S 5

∑

Total=

40 Total=

30 155

MSc C = 15 MSc C = 30 75

MSc S = 25 MSc S = 0 80

4

CORE COURSES

Course name Reference Systems in Space and Time

Semester/year 2/1

ECTS credits

Lectures:3 Practice/exercise: 2 Project: Total:5.0

Lecturer

Study hours

Lectures:75 Practice/exercise: 50 Project: Total: 125

Learning outcomes

After that course students will: 1. havea deep understanding of the definition and realisation of coordinate

reference systems 2. be able to transform between Earth-fixed and Celestial Coordinates 3. be familiar how to determine Earth Orientation Parameters 4. be able to distinguish between relevant time systems 5. have a basic knowledge in 4D time-coordinate systems used in Geodesy

Syllabus (List of lessons)

1. Inertial and Quasi-Inertial Systems 2. Celestial System 3. Dynamical and kinematical system realisations

(via VLBI, GAIA, Satellite Techniques) 4. International System of Quantities, SI Unit System, Derived Quantities

essential for Geodesy 5. Earth Fixed Coordinate systems 6. Transformation between celestial and Earth fixed system 7. Precision, Nutation 8. Polar Motion, dUT1, LOD 9. Time Systems 10. Relativistic models for Time and Coordinates 11. Continental Height Systems 12. Future Global Height System

Prerequisite Geodetic Reference Systems, Physics

Course literature

Jekeli, C., 2012: Geometric Reference Systems in Geodesy. Ohio State University, 209 pages. Mulić, M., 2016. Geodetic reference systems-lecture note. UNSA Sarajevo.

Assessment Examination, 5 credits, grade scale: 6 to 10

Grading

10 Excellent 91 - 100 9 Very good 81 - 90 8 Good 71 - 80 7 Satisfactory 61 - 70 6 Adequate performance 55 - 60 5 Unacceptable below 55

5

Course name Precise Positioning and Navigation

Semester / year 3/2

ECTS credits

Lectures: 2 Practice/exercise: 1 Project: 2 Total: 5

Lecturer

Study hours


Learning outcomes

After this course student will: 6. To get knowledge about the navigation and the applications of the global

navigation satellite systems. 7. Analyse the advantages and disadvantages of GNSS in the navigation

applications, especially qualification of errors by source and distances between reference and mobile receivers.

8. Got skill to use and apply GNSS equipment (hardware and software) for the navigation applications.

9. Got basic knowledge of augmentation satellites navigation systems: WAAS (USA), EGNOS (Europe) and MSAS (Japan), as well as the civil service DGNSS.

10. Got basic knowledge of different sensors integration in the outdoor and indoor navigation.

11. Cycle slips detection and repair 12. Ambiguities, the basic techniques for solutions of ambiguities 13. Apply of satellite navigation techniques in surveying and other purposes.


1. Basic of the navigation. State vector.

2. History of the navigation.

3. Terrestrial radio navigation systems. e-LORAN

4. Active and planned GNSS (GPS, GLONASS, Galileo, BeiDou-Compass...).

5. Navigation by satellite positioning. Methods of the positioning by GNSS: absolute and relative with different levels of accuracy. Local-Area Differential GNSS, Wide-Area Differential GNSS. Implementation of Wide-Area Differential, ie. Inmarsat Civil GPS navigation.

6. Transfer techniques and formats of the differential correction parameters with explanation of the advantages and disadvantages

7. Properties, method of use and capabilities of equipment for GNSS navigation applications.

8. Properties, method of use and capabilities of software for GNSS navigation application. Application of navigation devices in geodesy and geo-informatics.

9. SBAS and GBAS systems in navigation. WAAS (USA), EGNOS (Europe) and MSAS (Japan), as well as the civil service DGNSS.

10. Vulnerability of GNSS. Interference. Jamming.

11. Integration of different sensors in navigation. INS, Pseudo-satellites, A-GNSS, RFID.

12. Inertial navigation systems-INS.

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13. Application of the Kalman filters to predict the correction parameters and errors detection of dynamic systems.

14. Application of the navigation devices in transportation, environmental protection, agriculture, forestry, sports, recreation, etc.

15. Indoor navigation.

Prerequisite

Course literature

1. Hofmann-Wellenhof, B., Legat, K., Wieser, M.: Navigation-principles of positioning and guidance, Springer Wien New York, 2003.

2. Hofmann-Wellenhof, B., Lichtenegger, E., Wasle: GNSS Global Navigation Satellite Systems: GPS, GLONASS, Galileo and more, 2008.

3. Groves, P.D.: Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, 2008.

4. Mulic, M.: Satellite navigation, lecture note, FCE UNSA, 2016.

Assessment Project, 2 credits, grad scale: 6 to 10 Examination: written 2, oral 1 credits, grade scale: 6 to 10

Grading


Course name Physical geodesy

Semester / year 1/1

ECTS credits


Lecturer

7

Study hours

Lectures: 75 Practice/exercise: 50 Project:0 Total: 125

Learning outcomes

The aim of this course is to introduce students with the theoretical and practical concepts of physical geodesy, boundary value problems of the gravity potential theory and the mathematical models used to determine the geodetic reference surface. After this course students will be able to: describe and explain the effects of the gravity field and its importance for the modelling of geodetic reference surfaces, define and use different systems of heights, to model and to apply parameters of a datum transformation, to create mathematical model of the gravitational effect of topographic masses, to form and implement the mathematical model for the prediction of the parameters of the anomaly potential.


Introduction. Gravitational force. Gravitational potential. Spherical harmonic representation of the gravitational potential. Laplace equation. Poisson equation. Equipotential surfaces, verticals. Boundary value problems. Gravity of the Earth. Gravity potential. Harmonics expansion of the gravity potential. Gravity gradients. Normal gravity and normal potential. Harmonics Expansion of the normal potential. Normal gravity gradients. Anomalous gravity field. Stokes equation. Vening Meinesz equation. Numerical evaluation of Stokes’ equation. Gravity reduction methods (Free air,Bouguer, Poincaré-Prey,Helmert, Faye). Theory of Molodensky. Height systems. Statistical methods of Physical geodesy. Interpolation by least square collocation. Dedicated satellite missions. Global geopotential models.

Prerequisite No

Course literature

Heiskanen, W. A., and H. Moritz., Physical Geodesy, W.H. Freeman and Co., San Francisco, 1967. Bernard Hofmann-Wellenhof and Helmut Moritz, Physical Geodesy, Springer Verlag Wien New York, 2005. Torge W., Gravimetry, Walter de Gruyter, Berlin-New York, 1989.

Assessment Project, 3 credits, gradscale scale: 6 to 10 Examination, 2 credits, grade scale: 6 to 10

Grading


Course name Laser Scanning

Semester / year 2/1

8

ECTS credits

Lectures: 3 Practice/exercise: 2 Project: Total: 5

Lecturer

Study hours


Learning outcomes

This course will expand on remote sensing concepts with a focus on light

detection and ranging (LiDAR) technology. This course will cover the

fundamentals of LiDAR, explore current developments in LiDAR technology, and

discuss different applications where LiDAR is being used. The format of this

course will consist of lectures, lab assignments and readings. After completing this

course the students will be able to:

acquire theoretical and practical knowledge about the laser scanning measurement procedure, data management, processing and modelling;

understand principles of terrestrial, airborne and mobile laser scanning;

register point clouds taken from different positions;

georeference, segment and classify the point clouds;

fit geometrical primitives to point cloud;

create digital terrain models and urban models from laser scanning data;

map the images (textures) onto point cloud;

learn about applications of laser scanning in forestry, engineering and for cultural heritage.


Basic measurement principles and components of laser scanners. Airborne laser scanning (basics, ALS systems, operational aspects). Terrestrial laser scanning (basics, terrestrial laser scanners, operational

aspects). Mobile mapping. System calibration. Basics of LiDAR data processing and management. Point cloud structuring and visualisation. Registration and georeferencing of point clouds. Point cloud data formats and software tools. Accuracy, quality assurance and quality control of LiDAR data. Filtering of point clouds and DTMgeneration. Feature extraction from LiDAR data (roads, buildings, vegetation, etc.). Integration with other sensors. Laser scanning applications (forestry, engineering, cultural heritage,

etc.).

Prerequisite

Course literature

Vosselman, G. and Maas, H.-G.:Airborne and Terrestrial Laser Scanning, CRC Press - Taylor and Francis Group, 2010.

Shan J. and Toth. C.:Topographic LaserRangingAnd Scanning: Principles and Processing, CRC Press - Taylor and Francis Group, 2008.

Kraus, K.: Photogrammetry: Geometry from Images and Laser Scans, Walter de Gruyter, 2007.

Lerma García, J.L., Van Genechten, B., Heine, E., Santana Quintero, M.:Theory and practice on Terrestrial Laser Scanning, Editorial de la Universidad Politécnica de Valencia, 2008.

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Assessment Practice/exercise, 2 credits, gradscale scale: 6 to 10

Examination, 3 credits, grade scale: 6 to 10

Grading

10Excellent 91 - 100 9 Very good 81 - 90 8 Good 71 - 80 7 Satisfactory 61 - 70 6 Adequate performance 51 - 60 5 Unacceptable below 51

Geovisualization Course name

2/1 Semester / year


ECTS credits

Lecturer


Study hours

The major objective of this course is to learn principles of cartography and techniques for effective visualization of geographic data both spatial and spatial-temporal in 2D and 3D space using modern technologies for map creation and dissemination. Utilize a variety of thematic mapping and geovisualization techniques.

Learning outcomes

Cartographic fundamentals.

Visual variables: spacing, size, orientation, shape, arrangement, height, hue, value, saturation.

Mapping discrete features.

Treatment of continuous surfaces.

Introduction to thematic mapping.

Statistical mapping.

Space-time visualization and 3D visualization

Introduction to multimedia and web cartography.

Data models and data formats; Model based visualization

Standardization and formats KML, VRML, GEOVRML, CITYGML; WEBGL, glTF

Cartographic visualization for Web, SLD ;

Virtual globes.

Virtual reality - VR and augmented reality - AR

Smart cities.

Map mashups.

Volunteered geographical information.


No Prerequisite

Kraak, M. J., & Ormeling, F. (2011). Cartography: visualization of spatial data. Guilford Press.

Slocum TA, McMaster RB, Kessler FC & Howard HH (2009) Thematic Cartography and Geovisualization, 3rd edition. Pearson / Prentice-Hall.

Course literature

10

MacEachren, A.M, Taylor, D.R.F.: Visualization in modern cartography, Volume

2, 1st Edition

Jiang, B., & Li, Z. (2013). Geovisualization: design, enhanced visual tools and

applications. The Cartographic Journal.

MacEachren, A. M., & Taylor, D. R. F. (Eds.). (2013). Visualization in modern

cartography. Elsevier.

Kolbe, T. H., Gröger, G., & Plümer, L. (2005). CityGML: Interoperable access to

3D city models. In Geo-information for disaster management (pp. 883-899).

Springer Berlin Heidelberg.

Practice/exercise, 3 credits, gradscale scale: 6 to 10 Examination, 2 credits, grade scale: 6 to 10

Assessment


Grading

Course name Web-GIS

Semester/year 2/1

ECTS credits


Lecturer

Study hours


Learning outcomes

The aim of this course is to introduce students to the basics of Web GIS as a technology that allows users to deliver authoritative maps, analytics, and geographic information to a wider audience, using lightweight clients and custom apps on web and smart devices. After this course the students will be familiar with technology, principals and will be able to understand and apply web GIS from theoretical side and practical side.

Syllabus

List of lessons: 1. WEB GIS Introduction (definitions, basic components and applications). 2. Basics of networks and the Internet (communication models, protocols,

LAN, WAN). 3. Client-server architecture and distributed system architecture (Web client-

server architecture, DCOM, .NET, CORBA, Java). 4. Basics of client-side programming (HTML, CSS, DOM, JavaScript). 5. Basics of server-side programming. 6. Distributed GIS architecture, components and development. 7. Web GIS services (WMS, WMTS, WFS, WFS-T, WCS and WPS). 8. Standards for distributed GIS services.

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9. Commercial and open-sorce software, tools and components for develo-ping Web GIS applications.

10. Spatial Data Management in order to understand the basics of RDBMS ; 11. Aspects of quality and safety for distributed GIS. 12. Distributed GIS applications. Mobile GIS and real-time GIS

List of Seminars To be decided according of level of the students and GIS programs to be used

Prerequisite GIS basic

Course literature

[1] Pinde FU "Getting to know WEB GIS" second edition. Esri Press 2016 [2] Zhong-Ren Peng, Ming-Hsiang Tsou: “Internet GIS: Distributed Geographic Information Services for the Internet and Wireless Network”, John Wiley & Sons, 2003

Assessment Project, 2 credits, grad scale: 6 to 10 Examination, 3 credits, grade scale: 6 to 10

Grading


Course name Advanced Programming

Semester/year 1/1

ECTS credits


Lecturer

Study hours


12

Learning outcomes

After completion of this course students will gain knowledge and skills to write, test and debug multitired software systems. They will learn how to develop object-oriented middle tier application using Java programming language and Spring framework, data tier using PostgerSQL and client tier using selected JavaScript framework.


Kohesion. Coupling. Delegation vs inheritance.

Design patterns. Model View Controller.

Singleton. Adapter. Command. Composite. Observer. Abstract Factory.

Object-relational mapping (ORM). ORM using Java Persistence API (JPA).

Representational State Transfer (REST) architecture. REST web services.

Middle tier application implementation using Spring framework.

Web programming using HTML, CSS and JavaScript.

Web client application implementation using chosen JavaScript framework.

Prerequisite Object-oriented programming knowledge and basic relational database concepts knowledge.

Course literature

Gamma E., Helm R., Johnson R., Vlissides J., (1994), "Design Patterns: Elements of Reusable Object-Oriented Software", Addison-Wesley Professional.

Walls C., (2014), "Spring in Action" 4th Edition, Manning Publications.

Freeman A., (2014) "Pro AngularJS", Apress.

Assessment Written exam about theoretical knowledge: 30%. Multitier software system project (computer-based): 50% Multitier software system project: written paper and presentation: 20%

Grading

Local Grade Grade Cumulative % Definition

10-9 A 10 outstanding performance with only minor errors

8 B 35 above the average standard but with some errors

7 C 65 generally sound work with a number of notable errors

6 D 90 fair but with significant shortcomings

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5 E ~100 performance meets the minimum criteria

<5 F, FX

Fail – some more/ considerable further work required before the credit can be awarded


10 A 10 outstanding performance with only minor errors





5 F, FX


Course name Project management

Semester/year 3/2

ECTS credits


Lecturer

Study hours


Learning outcomes

Students will learn standard methodologies for project planning and management with a special emphasis on projects in the field of geodesy (projects at the national level and projects of less importance). They will learn project management methodologies and key stages: defining the objectives and scope of the project, projecting the budget and key resources, designing the methodology of project implementation, defining key activities, risk analysis, defining LFM - Logical Framework Matrix - key parameters for monitoring the project implementation and others.

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Defining the concept of a project, investment and infrastructural projects. Projects in various fields of geodesy. Legislation forpreparation of projects and technical documentation. Participants in the implementation of investment projects. Phases of the project from the investor's and perfomer’s standpoint. Standard methodology for managing projects. Project planning process. Management of project teams. Establishment of the project and scope of work. Structure of segregation of duties. The organization, responsibilities and powers of all project members. Planning of all key events and major activities. Making time-plans and WBS structure. Cost estimates and project budget. Plan of resource allocation. Plan and program of quality control, risk analysis, defining LFM - Logical Framework Matrix - the key parameters for monitoring the implementation of the project. Plan of protection and security. Plan of management. Procurement plan. Plan of termination. Plan of contorlling changes. Project reporting. Tender procedures on projects - public procurement procedures under domestic law, according to FIDIC, under EU law, according to WB procedures. Methods of assessment and evaluation of suppliers. Bank guarantees that accompany the implementation of projects. Methods of contract conclusion.

Prerequisite No

Course literature 1. Z. Gospavić: Management in geodesy, 2010, Faculty of Civil Engineering, Belgrade 2. P. Jovanović: Project management, 2008, FON Faculty, Belgrade


Grading

10 Excellent91 – 100 9 Very good 81 - 90 8 Good 71 - 80 7 Satisfactory 61 - 70 6 Adequate performance 51 - 60 5 Unacceptable below 51

Course name Research methodology and communication

Semester/year 3/2

ECTS credits


Lecturer

Study hours


Learning outcomes

The aim of this course is to provide an overview of theories and methods in research, with emphasis on methods in the field of technical sciences. The course will provide the students with necessary practical skills to manage their master’s thesis.

After this course the students will be able to demonstrate their understanding of the research process by: Formulate a thesis proposal

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Write a literature review Defend their thesis proposal Discuss someone else’s thesis proposal and thesis Design a small-scale social science research project Develop an outline for a master thesis Formulate the most suitable method for a specific research project


Research

Research Process

Research Design

Research Problem

Variables and their types

Formulation of Hypothesis

Sampling

Tools of Data Collection

Data Analysis

Interpretation of Data

Research Methods

Descriptive of Survey method

Experimental Method

Research Proposal

Research Report

Prerequisite No

Course literature

Pandey, P., Pandey, M.:Research methodology: tools and techniques, Bridge Center, Buzau, 2015 Kumar, R.: research Methodology, a step-by-step guide for beginners, SAGE, London, EC1Y 1SP, Third edition, 2011 Kothary, C.R.: Research Methodology, Methods and Techniques, 2nd revised edition, University of Rajasthan, Jaipur, New Age International, 2004 Johansson, RE.: Theory of Science and Research Methodology, Royal Institute of Technology, Stockholm, 2004 Kennett, B.: Planning and Managing Scientific Research, A guide for the beginning researcher, ANU Press, Canberra ACT 0200, Australia, 2014


Grading

10 Excellent91 - 100 9 Very good 81 - 90 8 Good 71 - 80 7 Satisfactory 61 - 70 6 Adequate performance 51 - 60 5 Unacceptable below 51

16

ELECTIVE COURSES

Course name Geodynamics and deformation analysis

Semester/year 2/1

ECTS credits


Lecturer

Study hours


Learning outcomes

1. The aim of this course is education of students for geodetic

monitoring behavior of object and ground and cooperation with

other profession that are deal with monitoring

2. Training studentsfor working of the geodetic monitoring projectof

the object andthe soil andthe reports of project realization..


1. Historical review of the development deformation analysis and

geodynamics.

2. General concepts of deformation and deformation causes.

3. The aimand objectives of geodynamics and deformation analysis.

4. Professions that are deal with monitoring.

5. Geodetic and geotehnical sensors.

6. Range of disaplecement which has to"certainly" discovered between

two epochs. Monitoring project of object (defining criteriaof

accuracy andreliability, the number and type ofmeasured values, the

choice of the datum ofthe network, calculation of accuracy, technical

conditions for the realization of deformation measurements,

sheadule of monitoring).

7. Time series andanalysisof time seriesin the time domain.

8. Basic conceptsof a dynamicsystem and process.

9. Modelsof deformation analysis-dynamic, static, kinematic

andcongruence. Fundamentals of mathematical statistics for the

application of the model congruence. Principles of model

congruence and review of world-recognized methods: Pelzer

methods.Robust methods and Karlsruhe method.

10. Specifics of geodetic observations: ground, dams, bridges, tunnels,

high buildings and landslides.

11. Monitor structure in both the vertical and the horizontal plane.

12. Networks for permanent monitoring of objects

Prerequisite Surveying, Engineering surveying, Advencend theory of adjustment

17

Course literature

Caspary,W. F.:Concepts of Networks and Deformation Analysis, Monograph 11, The University of New South Wales, Kensington, Australia, 1988 S. Ašanin and others:Work book of selected tasks from Engineering Surveying, Geokarta, Belgrade, 2007. (in Serbian) Mihailovic, K., Aleksic, I.: Deformation analysis of geodetic networks, Faculty of Civil Engineering, Belgrade, 1994 Milovanovic, B.: Linear and Nonlinear Modeling Geodetic Registred Deformation Processes of Structure, Doctoral Dissertation, Belgrade: University of Belgrade, Faculty of Civil Engineering, 2012.


Grading


Course name Advanced theory of adjustment

Semester/year 1/1

ECTS credits


Lecturer

Study hours


Learning outcomes

After completing the course the candidate shall be able to: 1. calculate generalized matrix inverses and apply the knowledge in free

network adjustment calculation 2. understand the parameter estimation concept, measures of accuracy, gross

errors detection, concept of reliability and variance components models, 3. analyze the free network dezign and manage the project documentation 4. continue education process at the PhD level


1. Gaus-Markof model, general and specific cases 2. Gaus-Markof model with rank deficiences 3. Generilized matrix inverse, pseudiinverses, minimum norm solution 4. Free network adjustment, datum problem 5. Accuracy measures of estimated parameters in geodetic networks,

eigenvalue decomosition 6. General Linear Hypothesis in Gaus-Markof models 7. Outlier detection, Data snooping, Pope tau test and Danish method 8. Concept of reliability, global and local 9. Variance components models

Prerequisite Theory of errors and adjustment theory

18

Course literature

Bozic, B.: Adjustment calculation - basic, The Faculty of Civil Engineering,Belgrade, 2012 Perovic, G.: Method of least square, Autor, Belgrade, 2005 Fan, H.: Theory of Errors and Least Squares Adjustment, KTH, 2006 Wolf,P. , Ghilani, C.: Adjustment computations, statistics and least squares in surveying and GIS, John Wiley&Sons, inc., 1997 Teunissen, P.J.G.: Adjustment theory - an introduction, Delft university of technology, 2003. Koch, K.R.: Parameter estimation and hypothesis testing in linear models, Springer-Verlag, Berlin, 1988


Grading


Course name Geodetic optimization

Semester/year 1/1

ECTS credits

Lectures: 2.5 Practice/exercise: 2.5 Project: 0 Total: 5

Lecturer

Study hours

Lectures: 62.5 Practice/exercise: 62.5 Project: 0 Total: 125

Learning outcomes

Students are able to perform numerical analysis of geodetic networks design, obtain variance solutions, and define quality criteria for geodetic networks from the standpoint of accuracy, reliability, sensitivity, economic feasibility, and application of optimization methods for the purposes of networks designing in geodetic survey.

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Theoretical teaching Definition of optimization. Introduction to optimal designing of geodetic networks. Classification of optimal geodetic networks designing. Zero, first, second and third order design. Geodetic networks quality criteria. Accuracy criteria in geodetic networks: global and local. Reliability criteria in geodetic networks: global and local. Matrix criteria. Optimization of zero-order geodetic networks design. Optimization of first-order design – previous analysis of accuracy and reliability. Optimization of second-order design – method of accuracy and reliability optimization. Application in networks design for geodetic survey. Practical teaching Application of optimization methods for designing geodetic survey networks and numerical analysis of design, criteria of quality, accuracy and reliability, global and local measures, homogeneity and isotropy.

Prerequisite None

Course literature

1. Mihajlović, K., Aleksić, R. I.: Concepts of Networks in Geodetic Survey – Monograph, Cartography Company “GEOKARTA” d.o.o. Belgrade, 2008. p. 725. (in Serbian) 2. Ninkov, T.: Optimization of Geodetic Networks Design. Naučna knjiga, Belgrade, 1989. 3. Opricović, S.: System Optimization. Nauka, Belgrade, 1992. 4. Mihajlović, K., Aleksić, R. I.: Deformation Analysis of Geodetic Networks. University in Belgrade, Faculty of Civil Engineering, Belgrade, 1994. p. 237. (in Serbian) 5. Fletcher, R.: Practical methods of optimization. John Wiley & Sons Ltd. 1990.

Assessment Seminar (50 points) and Written exam exam (50 points)

Grading First semester

Course name Integrated sensor technologies

Semester/year 1/1

ECTS credits


Lecturer

Study hours


20

Learning outcomes

After completion of the course, students have practical knowledge to implement integrated sensor system for a sensor platform according to standards, concepts and examples. Application is related to survey of geometrical and other characteristics of surface and subsurface structures of interest. Students obtain skills to apply software procedures that provide processing of data collected using integrated sensor systems.


1. Concepts and prerequisites for integration of remote sensing technologies: Ground Penetrating Radar - GPR, Geo Sensor Networks - GSN, Terrestrial Laser Scanning - TLS, Airborne Laser Scanning - ALS, Global Navigation Satellite Systems - GNSS, Inertial Navigation Systems - INS, Robotized total Stations - RTS, thermal cameras, digital cameras

2. Classification and capabilities of sensor platforms: levels of automation and motion control concepts for controlling the movement of a platform. Georeferencing of sensor platforms and sensors.

3. Integrated systems for terrestrial survey with fixed position of a sensor. Acquisition and data processing by integration of TLS, RTS and digital camera.

4. Integrated systems for terrestrial survey with variable position of a sensor. Acquisition and data processing by integration of GNSS, INS, TLS and digital camera.

5. Integrated systems for airborne survey with variable position of a sensor. Acquisition and data processing by integration of GNSS, INS, ALS and oblique camera.

6. Integrated systems for survey with unmanned airborne vehicle and combination of sensors (thermal camera, digital camera)

7. Integrated survey system of GPR and GNSS/TLS/video recording. Principles of georeferencing and processing of radargrams, point clouds and video recordings.

8. Integrated system of remote sensing sensors and GSN measurements in real-time and near real-time.

Prerequisite Knowledge and skills obtained in courses related to remote sensing, basic procedural programming and relational/spatial database knowledge, basic principles of sensor measurement.

Course literature

N. Madhu, R. Sathikumar, Satheesh Gopi, "Advanced Surveying: Total Station, GIS and Remote Sensing", Pearson Education, 2006.

George Vosselman, Hans-Gerd Maas, "Airborne and terrestrial laser scanning", CRC Press, 1010.

Takashi Fujii, Tetsuo Fukuchi, "Laser Remote Sensing", CRC Press, 2005.

A. Benedetto, L. Pajewski (Eds.), Civil Engineering Applications of Ground Penetrating Radar, Springer, 2015.

Valavanis, K., Vachtsevanos, George J. (Eds.), "Handbook of Unmanned Aerial Vehicles", Springer 2015.

Assessment

Practical exam: 50% o Four laboratory exams (computer and sensors based): 4 x 10% = 40% o One project: paper and presentation about the study case: 10%

Theoretical-practical exam 50% o Concepts and prerequisites for integration of remote sensing technologies:

15% o Integrated terrestrial survey systems with fixed and variable position of a

sensor: 20% o Integrated airborne survey systems with variable position of a sensor: 15%

Grading Local Grade Grade Cumulative % Definition

21

10-9 A 10 outstanding performance with only minor errors





<5 F, FX








5 F, FX


Course name Precise industrial measurements

Semester/year 2/1

ECTS credits


Lecturer

Study hours


22

Learning outcomes

The aim of this courese is education of students for geodetic worksin the industry(prefabricated construction of buildingsand assemblyof technological devices). After this course the students will be able to designing and realization of the project in industry.


Idustry as the activity field of engineering geodesy. Typesand characteristics offacilities andtechnological devices. Specifics ofprefabricatedconstruction of facilitiesand assemblyof technological devices. Norms andStandards. Technical requirements.Tolerances. Types ofstructural elementsof buildings. Selection of characteristic measurement points. Typesof geodetic networksfor prefabricated constructionof buildingsand assemblyof technological devices. Geodetic worksin the preparationof structural elements. Instruments andmethods of measurement: interferometer, autocollimator, extensometers, precision plumments, industrial total stations, laser tracker. Measuring robots in geodesy. Computer aided 3D survey. Short range digital photogrametry Control of geometryconstructed facilityof correspondencetesting. Control of geometryconstructed facilityby estimation parametersofgeometricelements. Testingof controlledgeometryusing linearhypothesis.

Prerequisite Surveying, Engineering surveying, Advencend theory of adjustment

Course literature

Сундаков, Я.А., Гeoдезические рабoты при возведении крупных

промышленных сооружении и высотных зданий, Недра, Москва, 1980

Баран, П. И., Геидезические работы при монтаже и экцплуатации

оборудования, Недра, Москва, 1990

Schofield, W., Breach, M,: Engineering surveying, Elsevier’s Science, Oxford, 2007. Moderne sensorik fur Bauvermessung, VDI, 1999


Grading


Course name Geodetic Space Techniques

Semester/year 3/2

ECTS credits

Lectures: 3 Practice/exercise: 2 Project: Total:5

Lecturer

23

Study hours

Lectures:75 Practice/exercise: 50 Project: Total: 125

Learning outcomes

After that course students will: 14. havean understanding in propagation of electromagnetic signal 15. understand the basic principles and observation types of the Geodetic

Space Techniques 16. understand and perform parameter determination from Geodetic Space

Technique observations 17. become familiar with related International Services


13. Review Electromagnetic Signal Propagation 14. VLBI Observation Model 15. Review in Satellite Motion 16. GNSS Observation Model 17. SLR Observation Model 18. Altimetry Observation Model 19. Atmospheric Effects in Signal Propagation 20. Parameter Determination from Geodetic Space Techniques 21. Synergy of Multi-Technique Parameter Estimation 22. International Services (IGS, IVS, ILRS)

Prerequisite GNSS positioning, Reference Systems in Space and Time, Physics

Course literature

Seeber G.: Satellite Geodesy, 2nd , 2008, de Gruyter Plag H.-P., Pearlman M. (eds.): Global Geodetic Observing System: meeting the requirements of a global society on a changing planet in 2020, 2009, Springer Fu L.-L.; Cazenave A.: Satellite Altimetry and Earth Sciences, International geophysics, vol. 69, 2001, Academic Press

Assessment Examination, 5 credits, grade scale: 6 to 10

Grading


Spatial databases and SDI Course name

1/1 Semester/year

Lectures: 2.5 Practice/exercise:2.5 Project: 0 Total: 5

ECTS credits

Lecturer

24

Lectures: 62.5 Practice/exercise:62.5 Project: 0 Total: 125

Study hours

After completion of this course candidates have sufficient knowledge in the field of spatial databases and spatial data infrastructures. To have basic and applied knowledge about principles, methods, implementation and operational management of spatial databases and spatial data infrastructures.

Learning outcomes

1. Database models and data modelling

2. Fundamentals of relational, object orijented, relational with object oriented extension and XML models and databases systems,

3. Spatial data models and spatial database systems

4. Spatial query languages, spatial storage and indexing, query procesing and optimization, spatial networks

5. Project managament and spatial database implementationSpatial data mining and decision suport systems

6. Trends of spatial database systems

7. Spatial data sources (public, open acces, commercial)

8. Spatial Data Infrastructures(SDI) – fundamentals, components

9. Background of SDI development

10. Standards in geomatics and SDI development

11. Spatial data infrastructure and policy development in Europe

12. INSPIRE data specifications

13. National Spatial Data Infrastructure


No Prerequisite

Spatial Databases – A tour, Shashi Shekhar, Sanjay Chawla

Spatial Database Systems – Desing, Implementation and Project Managament, Yeung, Albert K.W., Hall,G. Brent

The SDI Cookbook

Course literature

Practice/exercise, 2.5 credits, gradscale scale: 6 to 10 Examination, 2.5 credits, grade scale: 6 to 10

Assessment


Grading

Course name Spatial Analysis

Semester/year 2/1

ECTS credits


25

Lecturer

Study hours


Learning outcomes

After completing this course, the students of this class are expected to learn how to: 1. formulate real-world problems in the context of geographic information

systems and spatial analysis 2. apply appropriate spatial analytical methods to solve the problems 3. utilize mainstream software tools (commercial or open-source) to solve

spatial problems 4. communicate results of spatial analysis in the forms of writing and

presentation


1. Concepts in spatial analysis and spatial statistics 2. Representation of spatial data (fundamentals in spatial databases) 3. Analytical methodologies and model building 4. Core components of spatial analysis, including distance and directional

analysis, geometrical processing, map algebra, and grid models 5. Exploratory Spatial and Spatio-temporal Data Analysis and Data

Visualisation 6. Spatial Statistics including Spatial Autocorrelation and Spatial Regression 7. Point and Areal Pattern Analysis 8. Surface analysis, including surface form and flow analysis, gridding and

interpolation methods, and visibility analysis 9. Network and locational analysis, including shortest path calculation,

travelling salesman problems, facility location and arc routing 10. Geocomputational methods, including agent-based modelling, artifical

neural networks and evolutionary computing 11. Data Science and Analytics for Big Data

Prerequisite Working knowledge of at least one GIS software packages

Course literature

de Smith, Michael J., Paul A. Longley and Michael F. Goodchild (2013), Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools, 4th Edition. Available in both print and web (free) version at http://www.spatialanalysisonline.com Tonny J. Oyana, Florence Margai, 2016. Spatial Analysis: Statistics, Visualization, and Computational Methods, CRC Press, Taylor & Francis Group O’Sullivan, David, and David J. Unwin, 2010. Geographic Information Analysis, 2nd Edition. New York, John Wiley & Sons. Fischer, Manfred M., Getis, Arthur (Eds.) 2010. Handbook of Applied Spatial Analysis. Springer.

Assessment

Oral exam about theoretical-practical knowledge: 50%. Tasks, tests and seminars submmited during the course: 20% Practical exam (computer-based), using the provided files and software: 30% Spatial analysis project: written paper and presentation about the study case assigned to each group: OPTIONAL


10-9 A 10 outstanding performance with only

26

minor errors





<5 F, FX








5 F, FX


Geostatistics Course name

3/2 Semester/year

Lectures: 2 Practice/exercise:1 Project: 2 Total: 5

ECTS credits

Lecturer

Lectures: 50 Practice/exercise:25 Project: 50 Total: 125

Study hours

To get acquainted the students with the theoretical concepts and basic terminology of geostatistics and spatial statistical methods. The application of geostatistical methods by current software packages.

Learning outcomes

27

Each student should be able to model and present numerically the structure of spatial correlation of the observed phenomena, to choose and implement adequate method that performs spatial prediction of observed variables. In addition the student acquires the basis for the application of advanced spatial geostatistical interpolation methods in 3D and spacetime framework.

1. Introduction to geostatistics. The concept of spatial modeling. Theory of regionalized variable. Types of spatial variables.

2. The overview and division of interpolation methods. 3. Spatial prediction and interpolation. 4. Regression models for the assessment of the surfaces trends. Multiple regression. 5. Characteristics of spatial variability. Variogram and covariance function. Spatial

covariance, stationarity and ergodicity.Experimental variogram and experimental covariance function.

6. Variogram modeling by least squares method. The concept of anisotropy. 7. Theory of ordinary kriging, universal kriging, block kriging. 8. Quality assesment of geostatistical prediction. Kriging

variance. Crossvalidation. Quality assesmet using independent data sets. 9. The stochastic simulations. 10. Visualisation of predictions and uncertainties


No Prerequisite

Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. John Wiley & Sons.

Bivand, R., Pe esma, E. J., Go mez-Rubio, V. (2008). Applied spatial data analysis with R. New York: Springer.

Course literature

Project, 3 credits, gradscale scale: 6 to 10 Examination, 2 credits, grade scale: 6 to 10

Assessment


Grading

Course name Location-Based Services

Semester/year 3/2

ECTS credits


Lecturer

Study hours


28

Learning outcomes

The format of this course consists of lectures and lab assignments. After

completing this course the students will:

1. be familiar with principles, architecture and functioning of location-based services;

2. understand the place and role of geoinformation expert in this multidisciplinary field;

3. have knowledge on the technology used to develop and implement location-based services;

4. have skills to implement some components required for certain LBS applications.


1. LBS architecture. 2. Positioning and navigation for the needs of LBS (satellite navigation

using GPS, EGNOS and Galileo systems, radio positioning). 3. Telecommunications infrastructure (principles of wireless

communications, GSM, GPRS, and UMTS networks, WLAN). 4. Data structures and data management. 5. Distribution of data, protection of privacy, authorization. 6. Distributed geoinformation services for LBS. 7. Geocoding, reverse geocoding, mapping and visualization on small

screens of mobile devices, phones and PDA devices. 8. Classification of services (business-cases). 9. Positioning, monitoring, fleet management, personal applications, etc.

Prerequisite GIS

Course literature Hassan A. Karimi, Amin Hammad : Telegeoinformatics: Location-based Computing and Services, CRC Press, 2004.



Grading


Course name GIS in spatial planning

Semester/year 3/2

ECTS credits

Lectures: 1.8 Practice/exercise: 1.8 Project: 1.4 Total: 5

Lecturer

Study hours


29

Learning outcomes

After completed this course the graduate will: 1. know how to design different types of thematic maps and visualize three

dimensional geographical data models; 2. be able to use different ways of data interpolation; 3. apply different methods of spatial planning with a focus on land use and

environmental issues; 4. work with data models and analysing of networks; 5. be able to collect and integrate spatial data from various sources; 6. use analysis tools in GIS related to spatial planning; 7. know the spatial data policy issues.


1. Visualization of spatial data. 2. Interpolation methods in spatial planning. 3. Multi Criteria Evaluation (MCE). 4. Error propagation. 5. Modeling and analysis of networks 6. Spatial statistics in spatial planning. 7. Analysis tools - map algebra. 8. Geoprocessing data (buffering) in spatial planning. 9. Overlay techniques in spatial planning. 10. Model builder in spatial planning. 11. Spatial data policy issues – access, privacy, sharing, metadata 12. GIS within the fields of: local planning, regional planning, environmental

planning and transportation planning. 13. Using GIS to solve different tasks in spatial planning.

Prerequisite Basic skills in handling GIS software.

Course literature

1. Longley, Goodchild, Mcguire and Rhingl (2002). Geographic Information Systems and Science.

2. Huxhold, William E., Eric M. Fowler, and Brian Parr. 2004. ArcGIS and the Digital City: A Hands-on Approach for Local Government, ESRI Press.

Assessment

1. Attendance 0-10% 2. Seminar 0-15% 3. Partial Tests 0-30% 4. Final exam 0-45%

Grading


Course name Digital Photogrammetry

Semester/year 2/1

ECTS credits


Lecturer

30

Study hours


Learning outcomes

The format of this course consists of lectures and lab assignments. After

completing this course the students will:

1. have advanced knowledge on the principles and technology used in digital photogrammetry;

2. understand principles that automation methods in digital photogrammetry are based on;

3. have skills to use some of the digital photogrammetric software and hardware.


1. Introduction (terminology, tasks, relationship with other disciplines). 2. Basics of digital photogrammetry (systems for acquiring digital images,

photogrammetric scanners). 3. Digital photogrammetric workstation (basics, components, functions). 4. Fundamentals of image matching (basic problems and their solutions,

image correlation and least squares image matching). 5. Advanced methods of image matching (feature matching, relational

matching and template matching). 6. Epipolar geometry and calculating normalized digital images. 7. Automatic interior orientation. 8. Automatic relative orientation. 9. Automatic exterior orientation. 10. Automatic extraction of digital terrain models. 11. Automatic aerial triangulation. 12. Digital orthophoto production. 13. Products and applications of digital photogrammetry.

Prerequisite No

Course literature Toni Schenk: Digital Photogrammetry – Volume1, TerraScience, 1999.



Grading


Course name Advanced Remote Sensing

Semester/year 3/2

ECTS credits

Lectures: 2 Practice/exercise: 1.5 Project: 1.5 Total: 5

Lecturer

31

Study hours

Lectures: 50 Practice/exercise: 37.5 Project: 37.5 Total: 125

Learning outcomes

After completion of this course candidates have sufficient knowledge in the field of remote sensing and computer image processing. Knowing the theory and practice of remote sensing and its application Knowing the theory and practice about Active and Passive Remote Sensing Knowing the theory and practice of Hyperspectral images. Knowing the theory and practice of Radar images and LiDAR data. The students who complete this course will be able to adquire and process data in order to develop a remote sensing project using remotely sensed imagery from an active or hyperspectral sensor. This project involves: adquisition, pre-processing, classification, validation and/or change detection.


1. Advanced remote sensing applications 2. Hyperspectral remote sensing: Fundamentals. 3. Hyperspectral remote sensing: data gathering and preprocessing. 4. Hyperspectral remote sensing: data analysis and aplications. Validation of

the results. 5. Active sensors: Fundamentals.Active sensors (RADAR): data gathering,

preprocessing, data analysis and applications. Validation of the results. 6. Active sensors (LIDAR): data gathering, preprocessing, data analysis and

applications. Validation of the results. 7. Advanced remote sensing applications: project. Workflow. Data base.

Preprocessing. Information extraction. Validation and accuracy assessment.

Prerequisite None

Course literature

CAMPBELL, J. B. (2002). “Introduction to remote sensing” 3ª Edición. Ed. New York, The Guilford Press. JENSEN, J.R.; (1996), Introductory Digital Image Processing. A Remote Sensing Perspectiva, Ed.Prentice may. JENSEN, J.R (2000) Remote Sensing of the Environment: An Earth Resource Perspective, 2nd Edition, Prentice-Hall 544p Lillesand, T. M., Kiefer, R. W. and Chipman, J (2007) Remote Sensing and Image Interpretation (6th Ed). Wiley and Sons, New York

Assessment

Written exam about theoretical-practical knowledge: 20%. Tasks and seminars submmited during the course: 20% Practical exam (computer-based), using the provided files and software: 30% Remote sensing project: written paper and presentation about the study case assigned to each group: 30%

Written exam about theoretical-practical knowledge: 50%. Tasks and seminars submmited during the course: 15% Practical exam (computer-based), using the provided files and software: 35% Remote sensing project: written paper and presentation about the study case assigned to each group: OPTIONAL


10-9 A 10 outstanding

32

performance with only minor errors





<5 F, FX








5 F, FX


Course name Applied Mathematics

Semester/year 1/1

ECTS credits


Lecturer

Study hours


33

Learning outcomes

To acquire basic knowledge of Linear Algebra and some aspects of related numerical metjhods and the ability to apply basic algorithms to problems in geodesy Complex and real analysis as a tool to solve engineering problems, mathematical formulation of problems in science and engineering.


Lnear systems of equations: *Mathematical modelling by linear systems * Least squares solutions and orthogonalization (Gram-Schmidt and QR) * Diagonalization applied to linear differential and difference equations. * Numerical methods for solving linear systems of equations * Interpolation with polynomials and splines

Complex analysis: *Essentials. Derivatives. CR eguations. *Harmonic functions. Laplace equation. *Elementary functions. Analiytic functions. *Conformal mappings. Integral and Fourier transforms: *Properties. Applicationn. Laplace transforms: *Properties. Application. Partial differential equations: *Linear partial differential equations. *Classification of second order linear diferential equations in two independent variables

Prerequisite Knowledge of elementary calculus and basic in linear algebra.

Course literature

Gilbert Strang, Introduction to Linear Algebra, 4th ed., SIAM & Wellesley-Cambridge Press, 2009. M.Krasnov, A.Kiselev, G.Makarenko, E shikin, Mir Publishers, Mathematical Analysis for Engineers,1990. U.Ascher and C. Greif, A first Course in Numerical Methods", SIAM, 2011.

Assessment

Grading


Course name Real estate and investment analysis

Semester/year 1/1

ECTS credits


Lecturer

34

Study hours


Learning outcomes

Students will learn theoretical and practical knowledge of real estate market development, as a very important market of any economy. Students will understand the role and importance of all stakeholders, individuals and institutions, in the development process of transparent and open real estate market. They will also be able to formulate and analyze investments in real estate and review the economic problems of real estate investments and real estate maintenance.


Defining market concept, types and function of the market, supply and demand analysis, balance, changes in market equilibrium, elasticity and other market characteristics. Role of real estate market in national economy. Role of state control and its impact on real estate market. Legal basis for transparent real estate market developement.

Types of real estate: facilities for individual and collective housing, commercial and industrial buildings, land - agricultural, urban and other public buildings. Technical description of facilities, real estate location analysis, analysis and data collection for real estate value assesment.

Analysis of economic fundamentals as demand for real estate. Economic concepts and techniques of computation used for investment analysis: value, market value, price, property, time value of money (present, future), interest, tax policy with the fees and risk rates.

Prerequisite No

Course literature

1.Group of authors: Real Estate Business, Croatian Chamber of Economy, 2005. Zagreb, Croatia 2.D. Ling and W. Archer: Real estate principles a value approach, 2008. McGraw-Hill Univ. of Florida.

3. Corporate finance: Brealey/Myers/Allen: McFraw-Hill International Еdition

4. Real Estate principle-a value approach: Ling&Archer 5. Principles of Engineering Economy: E. L. Grant, W.G. Ireson, R.S. Leavenworth


Grading


Course name Land Consolidation

Semester/year 1/1

ECTS credits


35

Lecturer

Study hours


Learning outcomes

The aim of this course is to present the theoretical basis, models, practical solutions and experiences necessary for the realization of geodetic-technical worksin the process of land consolidation.

The course will provide the students with necessary theoretical and practical knowledge including skills necessary for the realization of the all works connected to land consolidation with emphasis on modern technologies

After this course the students will be able to demonstrate knowledge and understanding of the land consolidation process.


1. Spatial planning and land consolidation. Basic terms and definitions of land consolidation.

2. Investment program of land consolidation. Land consolidation as separate legal proceedings. Pronouncement of conducting the land consolidation. Determining the factual situation.

3. Surveying the area of interest for land consolidation. Classification and determining the value of agricultural land, building land and buildings.

4. Technical documentation in land consolidation.

5. Designing field roads. Road network scheme.

6. Grouping of agricultural estates. Determining the positions of buildings and new parcels.

7. Calculating the areas of project, parcels and buildings. Reduction of value for common needs.

8. Principles of distribution of land. Design of distribution and grouping the land.

9. Transforming the value of land to the areas of parcels. Calculating the elements of parcels.

10. Drainage. Position of drainage network.

11. Forest zones.

12. Environment protection.

13. Arranging and renewing the rural settlement.

14. Geodetic marking of buildings and parcels. Calculating and

determining the coordinates of points of buildings and parcels.

Designing the new geodetic network.

15. Closing pursuits and updating the cadastre. Organization of land

consolidation.

Prerequisite No

36

Course literature

Bullard, R.: Land consilidation and rural development, Angia Ruskin University, Cambridge&Chelmsford () UK R. Mihajlovic: Land consolidation, Faculty of Civil Engineering, Belgrade, 2011. Jacoby, E. Land Consolidation in Europe. International Institute for Land Reclamation and Improvement, Wageningen Virikainen, A.; An Overview of Land Consolidation in Europe Helsinki Universitz of Technologz, 2004

Assessment Exercise, 2 credits, gradscale scale: 6 to 10 Examination, 3 credits, grade scale: 6 to 10

Grading


modernizing geodesy education in western balkan with...

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