actionable proposals - copernicus in situ component · date: eea/idm/15/026/lot2 actionable...

EEA/IDM/15/026/LOT2

Actionable proposals

Doc. ID: EG-RPT-EEA-SC3-0051 Issue: 1.1 Date: 26/11/2019

Framework Service Contract EEA/IDM/15/026/LOT 2 for Services supporting the European Environment

Agency’s (EEA) implementation of crosscutting activities for coordination of the in situ component of the

Copernicus Programme Services

Call for tenders No EEA/IDM/15/026

Lot 2 Spatial data themes


Document Code: EG-RPT-EEA-SC3-0051 Issue: 1.1

Date: 26/11/2019

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1. Assessment of Core Reference Dataset (CRD) ......................... 5

1.1. Background information .................................................................................................... 5

1.2. Description of the proposal ................................................................................................ 5

1.3. Methodology ...................................................................................................................... 7

1.3.1. Quantitative criteria ................................................................................................... 7

1.3.2. Qualitative criteria ...................................................................................................... 9

1.3.3. Evaluation against service requirements ................................................................. 11

1.4. Results .............................................................................................................................. 11

1.4.1. Quantitative criteria ................................................................................................. 11

1.4.2. Qualitative criteria .................................................................................................... 26

1.4.3. Evaluation against service requirements ................................................................. 31

1.5. Conclusions ...................................................................................................................... 35

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[DOCUMENT RELEASE]

Name(s) Affiliation

Prepared by: Copernicus In Situ LOT 2 consortium

Contributions: André Stumpf, Stephanie Wegscheider, Marcos Pérez Osanz, Markus Probeck

GAF

Cristina Monaco ITHACA

Review : Francesca Lorenzon e-GEOS

Endorsement:

Approval: Henrik Steen Andersen EEA

Prepared for: European Environment Agency (EEA)

Represented by: (Project Manager)

Henrik Steen Andersen

Contract No. 3436/R0-Copernicus/EEA.57358 Implementing Framework Service Contract No EEA/IDM/15/026/Lot 2

[Change Record] Version Date Changes

1.0 2019-05-21 Initial complete version

1.1 2019-11-26 Revision according to feedback from BKG and IGN Spain

[Applicable Documents] ID Document

AD1. Framework Service Contract EEA/IDM/15/026/Lot 2

AD2. Specific Contract No. 3436/R0-COPERNICUS/EEA. 57358

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[Reference Documents] ID Document

RD1. EG-RPT-EEA-SC2_0043-Overview-of-Settlements_0.1

RD2. EG-RPT-EEA-SC1-State_of_Play_i0.9

RD3. InSitu Lot2_18-09-2018_MoM_i0r4

RD4. Technical specifications for implementation of a new land-monitoring concept based on EAGLE D4: Draft design concept and CLC-Backbone and CLC-Core technical specifications, including requirements review Version 4.0

RD5. BKG / EuroGeographics HO, Core Reference Dataset – CRD, Specification and Technical Guidelines – Prototype, 31.01.2019, p.13.

RD6. EA/IDM/R0/17/003Technical specifications for implementation of a new land-monitoring concept based on EAGLE D4: Draft design concept and CLC-Backbone and CLC-Core technical specifications, including requirements review. Version 4.0, 31.01.201

[Table of Acronyms]

Acronym Description

AOI Area of Interest

AP Actionable Proposal

BKG Bundesamt für Kartographie und Geodäsie

CEMS Copernicus Emergency Management Service

CLC+ Final product in the establishment of Corine Land Cover 2nd generation

CLMS Copernicus Land Monitoring Service

CRD Core Reference Dataset

EEA39 33 member and 6 cooperating countries of the European Environment Agency

ELF European Location Framework

ELS European Location Services

FN false negatives

FP false positives

INSPIRE Infrastructure for Spatial Information in the European Community

LC / LU Land Cover / Land Use

NMCA National Mapping and Cadastre Agencies

OSM Open Street Map

SoP State-of-Play

TP true positives

VHR Very High Resolution

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1. ASSESSMENT OF CORE REFERENCE DATASET (CRD)

1.1. Background information The State of Play of the Copernicus In Situ Component 2017 (SoP) Report has highlighted that the limited access to national datasets on transportation networks constitutes a limiting factor in particular for CLMS and CEMS. The main issues are related to either the lack of free access and limited harmonization of existing national datasets [RD2, p. 118]. As a consequence, Open Street Map (OSM) is currently often used by the service as a freely available and constantly improving reference dataset. Its quality, however, varies significantly from one location to another and does not always meet the requirements. The Core Reference Dataset (CRD) is a prototype vector dataset, which targets the integration of geospatial datasets that have been made available by European National Mapping and Cadastral Authorities (NMCA) under the umbrella of INSPIRE. EuroGeographics has initiated the compilation of the CRD comprising for now the INSPIRE themes Transport Networks (Road, Rail) and Hydrography at multiple spatial scales from 1:10.000 to 1:50.000. The current focus of this initiative is on the cross-border harmonization of datasets delivered by the NMCA. This activity is currently led by the Bundesamt für Kartographie und Geodäsie (BKG). A prototype of the dataset is readily available and the delivery of a preliminary product has been foreseen for April 2019. The CRD could constitute a valuable input dataset for several Copernicus services and in particular, the CLMS and CEMS could potentially benefit from a unique and consistent reference dataset with pan-European coverage. A coordinated feedback from the Copernicus services during this early production stage of the CRD is considered useful to support the elaboration of the CRD with the requirements of the services in mind. To this end, this AP targets the assessment of the existing prototype with respect to the requirements of the CLMS and CEMS.

1.2. Description of the proposal The currently available prototype dataset combines the hydrography and transport network for Austria, Czech Republic and Slovakia (Figure 1) and will be the main focus of this assessment. The dataset has been made available in a single ESRI FileGeoDatabase and comprises one point feature class (RailwayStationNode), three line feature classes (RailwayLink, RoadLink, Watercourse_L) and two polygon feature classes (StandingWater, Watercourse_A). Geographical names are provided in a separate table and linked via four Relationship Classes (RailwayStationNodeName, StandingWaterName, WatercourseLName, WatercourseAName) to the respective geometries.

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Figure 1: Example from the CRD prototype datasets depicting the transport network (top) and hydrography (bottom) at

the border between Austria, Czech Republic and Slovakia.

Both qualitative and quantitative criteria are considered to assess the characteristics of the dataset with respect to the service requirements. To this end, particular attention shall be given to the gaps

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highlighted in the SoP report. Section 4.1 [RD2] points out the difficulties that can arise when ingesting and harmonising national datasets into service component databases. The evaluation will thus specifically focus on the data model of the CRD considering not only thematic content (completeness and quality) but also level of completeness and standardisation of the documentation. In addition to the service requirements highlighted in the SoP, the evaluation will in particular consider the most recent versions of the Technical Specifications of CLC+ [RD4] and the CEMS Rapid Mapping/Risk and Recovery Mapping Technical Specifications. Other issues such as data policy, accessibility, timelines and spatial coverage must be left out of the evaluation since it is for example not yet clear how and if the CDR will be made available with Pan-European coverage.

1.3. Methodology The evaluation of the CRD was based on three pillars considering quantitative criteria, qualitative criteria and the evaluation against service specifications and requirements. The respective methodologies are explained in the subsequent sections.

1.3.1. Quantitative criteria

Visual checks

A total of 250 reference plots (Figure 2) with footprint of 100x100m were distributed randomly along the linear CRD features (roads, rivers, railways, lakes, etc.). To allow derivation of statistics for particular spatial subsets a stratified sampling designed was employed:

100 reference plots were distributed along the borders of the three countries. This was based on the consideration that a potential differences of the topographic data (thematic content, time stamp) in different countries could manifest in mismatching of missing features particularly along the borders.

Further 50 reference plots were distributed in each country, respectively, at least 100m away from the borders.

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Figure 2: Locations of the reference plots for validation in the CRD prototype area.

The respective plots were analysed considering primarily VHR IMAGE 2012, digital globes such as Google Earth and Bing Maps as well as Open Street Map evaluating the following criteria:

Completeness: Length of omitted and committed features. For each plot the VHR imagery, EU-Hydro and OSM were jointly analysed to check for differences in the amount (i.e. length) of features represented in the CRD and the alternative data sources. Detected differences were digitized according to their respective strata (Road trail, Railway, Watercourses, etc.) and only within limits of the reference plot. The quantitative analyses was subsequently carried out in terms of differences in length per total feature lengths within the plot. The working hypothesis was that the number of commission errors are likely negligible since CRD is based on national topographic maps.

Number of topological inconsistencies (e.g. missing connections, excess of connections). The topological errors considered where: lack of splitting of the linear features when approaching a junction and inconsistent flow direction (digitized direction) of rivers.

Geometric deviations from alternative datasets: Since the CRD will not cover all EEA39 countries missing areas would need to be complemented with alternative datasets. Given that OSM is a likely candidate (e.g. already used by many services) for the completion it was considered important to assess the geometric consistency among the two datasets. To this end a sample of 10 points were placed randomly along CRD features in each plot. For each point, the closest distance to the corresponding OSM feature was measured. If no corresponding OSM feature was available (approximately 30% of all cases) the VHR IMAGE 2012 was considered for the measurement instead. Based on the numerous measurements

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general statistics such as the average displacement and standard deviation can be calculated.

Line Density While a full independent assessment of the OSM dataset was not deemed relevant for this study a quantitative comparison of the density of the linear features in OSM and CRD was considered. To quantify differences in the amount of linear features (railways, roads and watercourses) between the CRD and the Open Street Map datasets, three density rasters (1 per each feature class) were calculated respectively for the two datasets at a cell size for the raster of 5000m. In light of the assessment being at national level and focused on regional differences in amounts of features, a raster size of 5000m was considered sufficient. For Open Street Map the latest version was extracted in SHP file format from the website https://download.geofabrik.de/europe.html. The resulting rasters were subtracted to facilitated the assessment of differences in the two datasets.

1.3.2. Qualitative criteria In addition, a qualitative analysis was carried out. The approach adopted for conducting a qualitative assessment of the CRD dataset consisted in choosing 7 areas of interest (AOIs, Figure 3) to analyse with the following characteristics:

there are at least 2 areas for each country (Austria, Slovakia and Czech Republic). One at the border and one inland.

Where possible, areas already used for services such as Rapid Mapping and Risk and Recovery have been preferred over newly delineated AOIs.

A variety of different scales, ranging from 1: 10 000 to 1: 100 000 have been considered, in order to analyse the dataset at different levels of detail and comply with the service requirements.

Areas in both urban and rural environments have been considered.

The AOIs are summarized with their respective service activations and scales in Table 1.

https://download.geofabrik.de/europe.html

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Figure 3: Location of the areas of interest

Table 1: Characteristics of the areas choosing for the analysis

AOI Name Activation ID AOI No. Scale Borders Transportation Hydrography

Austria Portschach am Worther See

EMSR340 10 1: 20 000 No No Yes

Salzburg EMSN005 Overview 1:100 000 Germany Yes Yes

Unterach am Attersee

EMSN005* N/A 1:10 000 No Yes Yes

Czech Republic

Prag EMSR045 Detail 01 1:15 000 No Yes Yes

Hodonin N/A N/A 1:30 000 Slovakia Yes Yes

Slovakia Vysoka pri Morave

N/A N/A 1:25 000 Austria Yes Yes

Povaszska Bystrica

N/A N/A 1:16 000 No Yes Yes

* Area delineated for the CRD assessment, but falling within the EMSN005 Overview AOI.

The qualitative assessment considers 4 principal indicators:

Thematic completeness of attributes and features. For every AOIs, thematic information of attributes were analysed to verify if there are available and understandable attributes and what is the percentage of completeness of these. Moreover, verify if all the thematic categories required by the service requirements are present.

Temporal consistency. How old are the features, and/or when were they last updated. If they are homogenously updated across the country.

Spatial consistency. How homogenous is the dataset across the country, especially at the borders. If there are gaps of information content and where.

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Documentation assessment. How complete and clear is the Metadata, the Lineage and other relevant documentation.

1.3.3. Evaluation against service requirements The quantitative and qualitative assessment will form the basis to evaluate if the CRD is compatible with the requirements of the CLMS and CEMS Mapping component (e.g. scale, homogeneity, mandatory attributes). This will also include the description of similarities, differences, and potential synergies with comparable datasets that are currently already used by the services (e.g. Open Street Map, EU-Hydro).

1.4. Results

1.4.1. Quantitative criteria

Completeness

The percentage of omitted / additional features was calculated in terms of length considering the total length of the confirmed CRD features within the reference plot as true positives (TP), the length of the omitted features (OF) and the length of additional features (AF). Accordingly, the percentages of omitted feature length and additional feature length are computed as formulated in Eq. 1 and Eq. 2.

𝑜𝑚𝑖𝑡𝑡𝑒𝑑 𝑓𝑒𝑎𝑡𝑢𝑟𝑒 𝑙𝑒𝑛𝑔𝑡ℎ =𝑂𝐹

𝑂𝐹 + 𝑇𝑃∗ 100%

Eq. 1

𝑎𝑑𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑓𝑒𝑎𝑡𝑢𝑟𝑒 𝑙𝑒𝑛𝑔𝑡ℎ =𝐴𝐹

𝐴𝐹 + 𝑇𝑃∗ 100%

Eq. 2

The results of this analysis are presented per strata and in total in Table 2. Generally, it can be observed that amount of additional features is negligible, while a limited amount of omissions could be detected. Omission is less than 2% for roads and 0% for watercourses in Austria and Slovakia. Small dirt tracks in rural areas dominate in the omitted road sections, whereas for Czech Republic the a significantly higher percentage was recorded (up to 12% for the RoadLink and 8,2% in the WaterCourse_L). The higher omission rates for Czech Republic can be mostly explained by rather small or particular features which do not fall within the technical specification of the CRD dataset (scale 1:10,000 to 1:50,000). This concerns for example very small watercourses (Figure 5) or roads on private property (e.g. access to industrial facilities or industries Figure 6, access to private properties Figure 7). If such examples are excluded from the analysis the errors for the RoadLink feature class for Czech Republic reduces to 1,4% which is in line with the results for Austria and Slovakia. The omitted river sections are in particular tributaries on the headwater areas or secondary seasonally dry streams. The slightly higher omission rates along the borders point toward a lower information density in those areas.

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Table 2: Percentage of omitted / additional features and average geographic displacements respect OSM data.

omitted feature length [%] additional feature length [%]

RoadLink Watercourse_L RailwayLink RoadLink Watercourse_L RailwayLink

Austria 1,2 0,0 0,0 0,0 0,0 0,0

Czech Rep 12,0 8,2 0,0 0,0 0,0 0,4

Slovakia 1,8 0,0 0,0 0,1 0,0 0,0

Borders 5,2 4,1 0,0 0,0 0,0 0,0

Total 5,0 3,1 0,0 0,0 0,0 0,1

Figure 4: Example for dirt track omitted in the CRD RoadLink feature class. Background left OSM, Background right ESRI

World Imagery.

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Figure 5: Example of small watercourses omitted in the CRD. Background left OSM, Background right ESRI World Imagery.

Figure 6: Example of omitted road giving access to railway and industry. Such cases may be not considered in the CRD

production

Figure 7: Example of omitted road that brings access to private properties. Such cases may be not considered in the CRD

production.

Topology

Given that the dataset is not yet accompanied by a description of the topological model full topological checks cannot be implemented at the moment. The assessment was thus limited to the analysis of inconsistencies of the roads splitting at junctions and the flow directions of rivers. In 27 of the 250 sample plots (10,8%), inconsistencies in either the river flow direction rivers or the segmentation of roads at junctions were detected. An example for inconsistent flow direction (i.e.

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digitized direction) is provided in Figure 8. For roads we noticed that roads are sometimes splits at junctions (e.g. Figure 9) and sometimes not (e.g. Figure 10) and no clear rule seems to prevail within the dataset. Additional information from BKG confirmed that up to the time of this assessment, topological rules concerning the flow direction and the splitting at road junctions had not yet been considered but will probably be taken into account for future developments.

Figure 8: Example of topological inconsitency. Inconsistency in flow direction (the river segments must flow in the same

direction) in the CRD.

Figure 9: Example of inconsistency in feature splitting in the unions. Here the main road is split.

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Figure 10: Example of inconsistency in feature splitting in the unions. Here the main road is not split.

Geometric deviations As explained above the geometric deviation between CRD and Open Street Maps has been assessed and for features not present in OSM (approximately 30%) ESRI Would imagery was used as a reference instead. The resulting mean deviations are 4 - 5m with a standard deviation of 3m to 9m Table 3. This is in line with the positional accuracy of 5 - 15m stated in the technical specification of the CRD prototype [RD5]. The high standard deviations highlight that in some areas the deviations between CRD and OSM can be significant (e.g. Figure 11) and are most likely due to generalisation in the CRD dataset. Both the average displacement as well as the variance of the offsets are slightly higher at the borders. This could be the result of both different input datasets for the CRD as well as different inputs and editors in the OSM, and should be taken into account when considering a merge national and/or OSM subsets across country borders. Table 3: Geometric deviation (mean and standard deviation) between the CRD and OSM in the different strata.

μ +/- σ [m]

Austria 4,16 +/- 6,97

Czech Rep 4,39 +/- 6,66

Slovakia 3,67 +/- 3,76

borders 4,98 +/- 8,77

Total 4,27 +/- 6,78

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Figure 11: Example of displacement between the CRD features and Open Street Maps (roads and watercourses)

Line Density Here the results of the comparison of the density rasters for railways, roads, and watercourse are presented. Railway The densities of features classes for railways from OSM and CRD result in some differences (Error! Reference source not found. and Figure 13). OSM has a considerably higher density of railways in the Czech Republic and slightly higher density in Austria, whereas in Slovakia, the two dataset show similar densities (see Table 4 and Table 5). In large parts, this is the result of the different mapping mode: CRD maps one line per railway and the number of tracks is stored in the attribute NumberofTracks whereas OSM maps every single track separately. However, the field number of tracks is in the present dataset only populated for Austria. For the Czech Republic this field was always <NULL> (which was set to 1 for the density analysis) and in Slovakia it was always 1, even though not all railways in the country are single track railways. Where available (i.e. Austria), the number of tracks was taken into account for the density analysis but the lack of this information leads to lower densities compared to OSM (mainly Czech Republic).

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Figure 12: Railway line length densities derived from the CRD dataset (top) and the OSM dataset (bottom)

Table 4:Statistics regarding railway length density in the CRD and OSM datasets

Mean [km/pixel]

StdDev [km/pixel]

Maximum [km/pixel]

CRD 3,62 7,59 187,14

OSM 4,69 10,95 313,42

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Table 5: Statistics regarding railway length density in the CRD and OSM datasets per country

Country Dataset Mean

[km/pixel] StdDev

[km/pixel] Maximum [km/pixel]

Austria CRD 3,15 7,41 187,14

OSM 3,62 10,32 313,42

Czech Republic CRD 3,50 4,42 43,86

OSM 6,32 12,38 193,32

Slovakia CRD 4,60 11,08 155,80

OSM 3,84 9,09 133,41

Figure 13: Railway length density differences between CRD and OSM. Reddish values correspond to higher values in OSM, while bluish ones represent higher values in CRD. Yellow pixels depict areas with approximately same densities in both datasets

Roads While the road densities resulting from CRD and OSM show similar spatial patterns (concentration cities and their surroundings, Figure 14) there are significant differences that reflect the generation process of both datasets. In particular, the much higher density in urban areas in the OSM dataset shows how OSM editors often focus on cities and thus create “hotspots” at a very detailed scale often including for example very small lanes and private property. The CRD on the other hand generalizes in a harmonized manner.

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Figure 14: Road density in the CRD dataset (top) and in the OSM dataset (bottom).

OSM does not only show higher maximum density values per pixel (983,69 km/pixel vs 447,36 km/pixel) and a higher mean value (106,51 km/pixel vs 85,21 km/pixel) but also a higher standard deviation (68,97 km/pixel vs 44,24 km/pixel, see Table 6). There are some differences between the three countries (see Table 7 and Figure 15). In Slovakia, on the one hand, CRD has a slightly higher density but overall values are most similar among the three countries, except for the maximum values, which manifests again the concentrated mapping efforts of OSM editors in and around cities. On the other hand, in the Czech Republic and Austria, OSM provides higher density values, again with particular emphasis of urban areas. Notable exception seems to be the Austrian federal state of Carinthia where CRD has higher densities (see blue area in the south of Austria in Figure 15).

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Table 6. Statistics regarding road length density in the CRD and OSM datasets.

Mean [km/pixel]

StdDev [km/pixel]

Maximum [km/pixel]

CRD 85,21 44,24 447,36

OSM 106,51 68,97 983,69

Table 7:Statistics regarding road length density in the CRD and OSM datasets per country


[km/pixel] StdDev


Austria CRD 110,82 52,58 447,36

OSM 132,89 71,61 983,69


OSM 99,31 63,35 966,35

Slovakia CRD 80,55 32,02 424,62

OSM 72,99 54,04 873,10

As mentioned above, the highest differences between CRD and OSM occur in the pixels with high density values: being mainly cities and their surrounding road networks. In these urban areas, OSM tends to map a significantly higher number of features than the CRD. This includes secondary streets and pedestrian areas (especially in the city centres), access to some infrastructures (airports, highways, railways, etc.), multiple roadways, etc. This is also illustrated in Figure 16. This shows that the mapping in OSM happens at a more detailed scale than in CRD. On the other hand, in some rural areas of Austria and Czech Republic, but in particular in the rural areas of Slovakia the CRD yields higher road densities. A more detailed example for such an area is provided in Figure 17 and the contrast between Slovakia and its two neighbours is also clearly visible in the differences of the densities (Figure 15). The contrast is likely results from a combination of higher information density of the CRD and lower information density of OSM in Slovakia when compared Austria and Czech Republic. As a result of the stronger contrast of the coverage of rural and urban areas OSM is also characterized by a higher variability than the CRD.

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Figure 15. Road density differences between CRD and OSM. Notice that most of the differences occur in the cities. Reddish values correspond to higher values in OSM, while bluish ones represent higher values in the CRD. Yellow pixels depict areas with approximately same densities in both datasets.

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Figure 16: Comparative images in the city centre of Vienna and the airport of Bratislava showing the higher density of features of the OSM in an urban scenario respect the CRD.

Figure 17: Example of an area (east from Levice, Slovakia) where the CRD has a higher density value than OSM.

Watercourses CRD shows a higher mean length of watercourses per pixel than OSM (Table 8) suggesting a generally more comprehensive coverage of water courses in the CRD when compared to OSM. OSM, on the other hand, has a higher maximum density, which stems from some hotspots in the western and central parts of Austria or along some borders of the Czech Republic (Figure 18).

Table 8: Statistics regarding Watercourses length density in the CRD and OSM datasets.

Mean [km/pixel]

StdDev [km/pixel]

Maximum [km/pixel]

CRD 34,67 13,02 102,21

OSM 25,79 14,81 194,95

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Figure 18: Watercourse density in the CRD dataset (top) and in the OSM dataset (bottom).

On country level, CRD and OSM have quite similar densities in the Czech Republic while CRD is much more comprehensive in most parts of Austria and Slovakia (see Figure 19). This also reflects in the mean densities in each country which are similar for the Czech Republic but quite different for the other two countries (see Table 9). The maximum is always higher in the OSM dataset which shows once again the issue of very detailed but spatially limited hotspot mapping of OSM editors.

Table 9: Statistics regarding Watercourses length density in the CRD and OSM datasets per country


[km/pixel] StdDev


Austria CRD 38,39 14,63 94,87

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OSM 20,67 12,78 154,95


OSM 35,12 12,93 131,31

Slovakia CRD 30,71 11,36 73,81

OSM 19,56 13,25 79,51

Figure 19: Watercourse density differences between CRD and OSM. Reddish values correspond to higher values in OSM, while bluish ones represent higher values in CRD. Yellow pixels depict areas with approximately same densities in both datasets.

Intersecting areas To quantify the amount of differences for the polygon features (water bodies) between the CRD and the Open Street Map Data, an intersection between both datasets was performed. The respective feature classes from the CRD are Watercourse_A (polygons delineating the watercourses beyond a certain width) and StandingWater (polygons associated to the watercourses, e.g. reservoirs and some other not topologically connected e.g. lakes, lagoons, ponds, etc.). Both feature classes we merged before intersecting them with the corresponding OSM features. The dataset of water bodies of the OSM included only features from the “gis_osm_water_a” SHP files which corresponds to the tag natural=water of OSM. OSM features classified as “glacier” or “wetlands” were excluded from the analysis, though, as those classes are out of scope of the CRD.

Table 10: Total area, intersecting areas and % of intersecting areas in the two datasets.

Total area [ha] intersecting area / total area [%]

non-intersecting area [ha]

non-intersecting area / total area [%]

CRD 242258,96 90,16 23847,50 9,84 OSM 288549,74 75,69 70138,27 24,31 Intersection 218411,46

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As can be seen from Table 10 the total area of water bodies in the OSM dataset is roughly 46.000 ha higher than in the CRD. While most of CRD water bodies areas are also represented in the OSM (90,16%), there are 24,31% OSM water bodies that are not depicted in CRD.

This reflects both the differences in the scale of mapping as well as different standards of miminum mapping width. Figure 21 compares the waterbodies captures as polygons in both datasets: purple color depicts waterbodies present in both datasets while blue and red depict waterbodies present only in CRD and OSM respectively. Detail map 1 in Figure 21 is an example where tributary rivers were captured as polygon features in CRD but are only available as line features in OSM. Detail map 2 shows an example of small waterbodies not captured in CRD due to a different scope of mapping scale.

Figure 20: Waterbodies for the whole area of interest. CRD in blue on the top and OSM in red at the bottom.

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Figure 21: Waterbodies in both datasets (purple), only in CRD (blue) and only in OSM (red). The detail maps show examples of tributary rivers only captured in CRD (1) and small waterbodies only captured in OSM (2)

1.4.2. Qualitative criteria

Thematic completeness of attributes and features

In the majority of cases, attributes are either always filled within a country or not at all. The cases in which fields are only partially completed in all countries are NationalRoadCode in RoadLink and the geographical names for the hydrographical features (Table 11). In general, features with filled attributes are those with a particular relevance. For example, the most important rivers in the country have an associated geographical name. Main roads, first, second and many third class roads have a National road code assigned in the attribute table, whereas many smaller roads do not.

The completeness of the dataset’s attributes was also calculated per country considering the fraction of fully completed attributes with respective to the total number of attributes. The results are shown in Figure 22. In general, the histogram highlights that Austria is the most complete in terms of themes and information, followed by Slovakia and finally Czech Republic. In particular, in Austria, compared to the other countries, the feature class RoadLink results as the most complete where also NumberOfLanes and MinMaxNumberOfTracks fields are described. Table 11 shows in which countries the listed fields are filled (the countries are specified with different colour) and to which degree (e.g. Empty = No attribute is described per countries, Complete = the attribute is described in all countries, Incomplete = the attribute is partially described in the information table).

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Figure 22: Level of completeness of the CRD dataset by feature class and by country. Only fully filled-in fields are

considered over the total number of fields in each attribute table.

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Table 11: Completeness of the attributes in the different feature classes and countries.

The relational Geographical Names table is not always complete. Across all analysed AOIs the transportation features classes RoadLink or RailwayLink have no related geographical name and only the RailwayStationNode has a corresponding entry in the geographical name table for each feature. For the hydrography classes, there is a partial correspondence between the features and the Geographical Names table. As shown in Figure 23, the naming of the hydrographic features in Czech Republic is rather complete, while the other are less complete and Slovakia, for example, is lacking a naming in the Watercourse_A class1. It is furthermore important to underline that almost all rivers at international borders have a toponym.

1 BKG informed that this error will be corrected in a future version of CRD.

AT SK CZ AT SK CZ AT SK CZ AT SK CZ AT SK CZ AT SK CZ

COUNTRY

INSPIREID

BEGINLIFESPANVERSION

ENDLIFESPANVERSION

HYDROID

PERSISTENCE

LEVEL

WIDTHLOWER

WIDTHUPPER

FICTITIOUS

VALIDFROM

VALIDTO

NATIONALROADCODE

EUROPEANROUTENUMBER

MINMAXNUMBEROFLANES

NUMBEROFLANES

MEASUREDROADPART

WIDTH

FUNCTIONALCLASS

FORMOFWAY

VERTICALPOSITION

CURRENTSTATUS

RAILWAYLINECODE

RAILWAYTYPE

MINMAXNUMBEROFTRACKS

NUMBEROFTRACKS

LEGEND

RailwayLink RailwayStationNode

TransportationHydrography

Watercourse_L Watercourse_A RoadLink

Complete

Incomplete

Empty

StandingWater

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Figure 23: Correspondence of geographical names with the attributes for the feature classes related to hydrography. The

results are in percentage.

Regarding the thematic categories, the transportation feature classes provide a high level of detail as they distinguish 9 different categories of roads (categorization different from that of OSM). Unfortunately, some infrastructure types such as harbours, and airfields (which are required by the CEMS and would constitute valuable input for the CLMS) are not present. But bridges and tunnels can be selected using the attribute “verticalPosition”. Regarding the hydrography dataset, there are no thematic categories (lake, river, canal), but information regarding persistence (intermitted, perennial) and level (underground, on the ground). According to BKG, this issue can be addressed in a future release of CRD by introducing the attribute “origin” with the values “manmade” and “natural”. This is considered a good solution. As detailed in a previous report [RD1] several services require up-to-date information on settlements such as built-up area, building blocks or individual buildings. The theme is currently not covered by the CRD.

Temporal consistency For Copernicus Services (CEMS and CLMS), the last update of the data is a key information to guarantee the most up-to-date service possible. For the CEMS the reference datasets should not be older than 2 - 3 years from the date of activation of the service. For CLMS products the dataset should ideally match the respective reference year with a tolerance of ± 1 year and an update frequency of currently 3 years. Unfortunately, the timing of the last update is missing (in the metadata report and in the attribute table). The attribute beginLifespanVersion, which should provide this information, is currently referring to the same 2018 date for all of Austria and Czech Republic, respectively. Only in Slovakia there is some variability in the dates within the dataset (ranging from 2002 to 2018).

Spatial consistency

Some differences in the spatial densities of linear features from one country to the other and some topological issues have already been described above in section 1.4.1. Apart from such issues the

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cross-border consistency of linear features (e.g. matching of roads at the borders) is generally good. There are, however some anomalies related to watercourses. Specifically, segmented or multipolygon features have sometimes incomplete thematic descriptions. Figure 24 shows an example of an areal watercourse (Watercourse_A) which is at the border between Austria and Slovakia. In this case, the name of the river is present only in the Austrian portion while in Slovakia it is missing2.

Figure 24: Example of a Watercourse feature in the area of Vysoka pri Morave at the border between Austria and Slovakia.

Figure 25 presents another example where a linear watercourse in Austria has a geographical name (feature in red) whereas it is surrounded by features without geographical naming3.



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Figure 25: Example of a Watercourse feature in the area of Vysoka pri Morave with missing naming.

Documentation assessment

The CRD dataset is provided together with a lineage document and with a metadata file in .xml format. The information is quite complete and exhaustive.

The Lineage document provides accurate specifications on the general properties of the dataset, and relevant contact information. It however never mentions the date and time-stamp of the product’s validity nor when a future updates are planned (every 3 years, personal communication Angela Baker, EuroGeographics). Further details on the product specifications could include items such as Minimum Mapping Widths (e.g. for WaterCourse_A) or the applied criteria for considering the inclusion / exclusion of permanent water bodies. Given the inherent differences of the input datasets used in the creation of the CRD it might be useful to document the specifications also per country to make the user aware of possible residual inconsistencies4.

1.4.3. Evaluation against service requirements

CLMS

4 BKG informed that this will be the way forward for future releases. Metadata will be provided for each country separately stating the

national specifics.

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Linear networks including the transport network and the hydrography are used for the production of several CLMS products. Geospatial data on the transportation network for example are essential inputs for the delineation of functional units in products of the Local Component (e.g. Urban Atlas, Natura 2000) and are also relevant for deriving training and validation of HRL Imperviousness Layer. Currently OSM is the most commonly used dataset in this context due to its free availability, completeness and constant updating. However, OSM has the disadvantage that it is not approved by an official authority. If produced homogenously with frequent updates for the whole of Europe, an approved dataset such as the CRD could constitute an interesting alternative, which might improve the acceptance of derived products. The current technical specifications of CLC+ mention transportation networks and hydrography as an indispensable input for the generation of the first level object borders delineating persistent landscape objects (“hard bones”). Those borders shall be derived from ancillary data on roads, railways and rivers and a series of criteria for such data are defined (RD6.). These criteria are presented in Table 12 and assessed against the characteristics of the CRD dataset. Table 12: Overview of criteria for ancillary data input for the CLC-backbone and the corresponding compliance level of the CRD prototype with the categories fully compliant, rather compliant, not fully compliant, not compliant and unknown defined.

CLC+ Specification Compliance level of the CRD

Scale: 1:10.000 or better rather compliant: scale range 1:10 000 to 1:50 000, 1:5000 to 1:10 000 in some cases

Geometric accuracy: +/-5m rather compliant: 5 – 15 m positional accuracy

Geometric representation: Centre line, parallel lines only at distances beyond 20 m

rather compliant: roads and in particular rail roads are represented at much shorter distances, which however is true for any datasets at this scale (needs to be addressed during pre-processing at production level)

>95% completeness including roads to agricultural tracks

rather compliant: only for border areas the quantitative results indicate that completeness could be slightly lower

connected network without dangles rather compliant: cul-de-sac are typically depicted in any transportation network datasets (needs to be addressed during pre-processing at production level)

Standardized types and information on tunnels and bridges for road and railways

fully compliant: 8 railway types, 10 road functional classes and 15 road types are distinguished, the verticalPosition attribute can be used to distinguish on ground, above ground and underground

All river with catchments larger than 10 km² should be included

fully compliant: detected omissions seem limited to smaller headwater streams

Provision of data at no or at reasonable costs

unknown: production costs would probably need to be covered but pricing model has not been fully established yet

Support of the full, free and open data policy of the end product

unknown: production costs would probably need to be covered but pricing model has not been fully established yet

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Provision of data to industrial service providers, potentially outside the country of the data provider.

fully compliant: included in standard end user license agreement

Availability of the data to the service provider at the start of the project implementation.

not fully compliant: might not be in time for tender CLC+ tendering phase

Coverage of the full EEA39 not compliant: first priority is currently given to completion of EU28

Temporal update frequency (CLC backbone 3-6 years)

fully compliant: targeted update frequency of 3 years

Table 8 shows that the CRD is fully or rather compliant with most of the requirements, which are currently established for the CLC+ backbone. However, there are some main obstacles which are likely to complicate its use for this purpose being mainly the limited spatial coverage at the moment (due to the early prototype stage of the CRD) and also the envisaged final spatial coverage for the EU28. For the production of the CLC+ for all EEA39 countries it would likely imply considerable additional effort to consistently combine CRD data for some parts of the AOI with OSM and other data sources. The pricing and licensing model of the CRD is currently not fixed and open for discussion, which creates some uncertainty regarding its possible use by the Copernicus Services. Further CLMS LC/LU products such as HRL Forest, Riparian Zones or Natura2000 do not strictly require transportation networks and are those not directly relevant for the evaluation of the CRD in the context of this study.

CEMS

For the Copernicus Emergency Management Service, reference datasets containing, amongst others, also transportation and hydrographical networks are fundamental for conducting objective analyses and comparing post disaster with pre-disaster situations. Currently, the most common reference dataset used for such activities is Open Street Map (OSM), which is also the most updated and complete. However, being crowd sourced spatial information, it does not have authoritative cartographic data. CRD would therefore be an optimal alternative for this type of service. In terms of thematic description and feature attributes, there are some differences between reference data used for CEMS and those present in CRD:

1. Road network classification based on the INSPIRE standard differs from that of Open Street Map. The functional class in the RoadLink feature class (CRD) is a relative classification system, based on the scale of importance of the road compared to the main road. In CEMS adapted datasets, road classification is absolute, based on the width of the road, the number of lanes it has and the type of material it is made of (asphalt, dust road etc.). This would imply that the service would need to adapt its dataset model to the INSPIRE standard, at least when activated within a European country.

2. In the attribute table of the feature RailwayLink (CRD), the number of tracks is not available for all countries: in the Czech Republic the value is <NULL>, in Slovakia it is always 1, even where there are more than one track, e.g. in railway stations, and only in Austria, the attribute “number of tracks” is partially filled with realistic values

3. In the CRD there is no distinction between natural and artificial water bodies, nor between rivers and streams. Only the width of watercourses is described, but it is not complete in

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all countries. This information is key for CEMS especially in disasters such as floods and tropical cyclones, where it can be asked to monitor reservoirs or to select only certain watercourses based on their relevance or dimension.5

4. Other attributes present in the CRD but not currently used for Rapid Mapping could be of interest for certain activations. Particularly, fields such as VerticalPosition and CurrentStatus in transportation feature classes are potentially useful information for the end-user. In addition, the Persistence field in hydrographical feature classes is crucial in the context of extreme meteorological events.

Table 13 shows a comparison between the main and relevant attribute fields for the CEMS as recorded in OSM and CRD. Table 13: Comparison between OSM features and CRD dataset

OSM CRD

Hyd

rogr

aph

y

Area

Name of the feature gis_osm_water_a_free StandingWater, Watercourse_A

Categories dock, glacier, reservoir, river, water, wetland Immanent to feature types: lakes and reservoirs, big rivers

Presence of geographic name yes yes, relational table

Line

Name of the feature gis_osm_waterways_free Watercouse_L

Categories canal, drain, river, stream not present


Point

Name of the feature not present not present

Categories not present not present

Presence of geographic name not present not present

Tran

spo

rtat

ion

Area

Name of the feature gis_osm_transport_a_free not present

Categories bus_station, bus_stop, ferry_terminal, railway_halt, taxi

not present

Presence of geographic name yes not present

Line

Name of the feature gis_osm_roads_free, gis_osm_railways_free RailwayLink, RoadLink

Categories Bridleway, cycleway, footway, living_street, motorway, motorway_link, path, pedestrian, primary, primary_link, residential, secondary, secondary_link, service, steps, tertiary, tertiary_link, track, track_grade1, track_grade2, track_grade3, track_grade4, track_grade5, trunk, trunk_link, unclassified, unknown funicular, light_rail, miniature_railway, monorail, narrow_gauge, rack, rail, subway, tram

main roads, firstClass, secondClass, thirdClass, fourthClass, fifthClass, sixthClass, seventhClass, eightClass, ninthClass, FormOfWay RailwayType

Presence of geographic name yes not present

Point

Name of the feature gis_osm_transport_free RailwayStationNode

Categories bus_station, bus_stop, ferry_terminal, railway_halt, railway_station, taxi, tram_stop

not present


Note: Categories in italic were not included in the density analysis in chapter 1.4.1.

5 According to BKG, the attribute “origin" can be included in CRD in the future. The values of this INSPIRE attribute are: manmade or

natural. This will allow to distinguish Watercourse into rivers and canals, and to distinguish StandingWater into lakes and reservoirs.

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1.5. Conclusions This study provides an evaluation of the CRD prototype currently covering Austria, Czech Republic and Slovakia. Based on the quantitative and qualitative assessment it can be stated that the prototype provides comprehensive and consistent information on the transportation and hydrographic network in terms of geometric features. Based on the quantitative assessment it can be stated that the CRD meets its own specifications (as provided together with the dataset) in terms of feature classes, scale and geometric accuracy. Visual assessments based on VHR imagery suggest a limited amount of omission of road features which is generally below 5%. Compared to OSM the CRD tends to provide a lower road density in urban areas, whereas in rural areas (particularly in Slovakia) the CRD tends to be more detailed. Generally, the NMCAs follow clear selection criteria for inclusion of features in road layers of the CRD. While this is already reflected in a more harmonised generalization at a scale from 1:10.000 to 1:50.000 it would be useful to document the selection criteria and potential differences among the national input datasets in order to support the interpretation of the data by the users. Regarding the hydrographic network (linear features), CRD and OSM provide similar densities in Czech Republic whereas in Austria and Slovakia, CRD tends to provide a denser representation. Hydrographic polygon features provide a mixed picture: small waterbodies are much more abundant in OSM being too small and thus out of scale to be captured by CRD. On the other hand, many tributary rivers which are consistently captured as polygons in CRD, are not included as polygons in OSM but merely as line features. The standards for mapping the railway network in OSM and CRD are very different: while in CRD the railway is always mapped as one line with the number of tracks theoretically captured as an attribute, each track is mapped separately in OSM. Despite this different mapping mode, the network density is similar in both datasets in Slovakia. In Czech Republic and Austria, though, the network is denser represented in OSM. The topological consistency of the CRD dataset is generally satisfactory while some improvements could be envisaged, such as the assignment of the proper flow direction (digitized direction = flow direction) to rivers and a more consistent handling of geometric splits at junctions. The data model of the CRD can be considered suitable for a structural compliance / compatibility with the INSPIRE data model. However, the level of completeness of the necessary attribute fields is currently hindering INSPIRE compliance and reduces the value of the dataset for the Copernicus services. This concerns for example the lack of time stamps. While the necessary INSPIRE attributes are filled, they currently contain the delivery dates rather than the update dates from the source data. According to BKG time stamps at the level of individual geometries are typically not feasible and time stamps will rather be provided at the country level in the future. Further attribute-related issues that have been noticed are a lack of geographical names for many features and the generally very variable completeness of the attributes. According to BKG the list of attributes will be revised in future releases according to the availability of the information from NMCAs. The OSM data model, neither is compliant with the INSPIRE data model but currently provides a greater level of detail in terms of attribution of the transport network. Thematically the CRD currently focuses on the transportation and the hydrographic network only. From a Copernicus service perspective, such information can be valuable for CLMS products (e.g. Urban Atlas or the upcoming CLC+) and CEMS activations. Considering for example the currently defined requirements for the CLC+ backbone it can be concluded that the CRD satisfies most

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requirements, whereas limitations exist due to limited spatial coverage and uncertainties regarding the pricing and licencing model. In this context it is important to highlight that the current prototype version of the CRD was compiled to provide an example of the possibilities of the dataset for evaluation purposes. According to BKG the dataset can be expanded reasonably easily and quickly if there is a confirmed demand for the data. This in turn would also require a more detailed understanding of the potential users and their requirements. As an authority-approved dataset the CRD might provide advantages in terms of acceptance of the resulting products by the NMCAs. However, in terms of completeness, thematic depth and updating frequency, the freely available OSM currently tends to provide the more satisfactory solution. From a CEMS-perspective it would be desirable to further enrich the CRD with a more fine-grained representation of transportation infrastructure (e.g. airfields, harbours and bridges), and further improve the completeness of the attributes mentioned above. While bridges and tunnels can already be distinguished based on the verticalPosition attribute other traffic themes such as air transport, harbours, bus stations or ferry terminals could be considered for future releases. Similarly, the value of the CRD for the Copernicus Services could also be further improved through the inclusion of LC / LU, whereby in particular information on settlements (built-up area, building blocks, buildings, population) appears a frequently requested theme among the services and could be included in future releases. The spatial coverage, updating frequency, pricing and licensing models of the CRD dataset may require further investigations and iterations involving EuroGeographics, EEA and potential users.