Urban Atlas DELIVERY OF LAND USE/COVER MAPS OF MAJOR EUROPEAN URBAN AGGLOMERATIONS.

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1 DELIVERY OF LAND USE/COVER MAPS OF MAJOR EUROPEAN URBAN AGGLOMERATIONS. (V 2.0) ~ November 2011 ~ 27 rue du Carrousel - Parc de la Cimaise Immeuble I VILLENEUVE D ASCQ info@sirs-fr.com - Site Internet : S.A.S. au capital de RCS LILLE APE 6311 Z - N d identification FR SIRET

2 SUMMARY Document identification 6 Document release sheet 6 Approval network 6 List of abreviations 7 Executive summary 8 1. Background Nature and purposes Related Documents Input Output List of LUZs produced Urban Atlas Methodology (extract of Mapping guide v1.1) Pre-processing and geometric adaptation of COTS navigation data Pre-processing of Topographic Maps Classification and Interpretation Application of FTS Sealing layer Interpretation rules Minimum mapping unit Priority rules Legend Table Encountered difficulties and solutions Adjustments to the "Mapping Guide" Interpretation rules Minimal Mapping Unit (MMU) Typology : post "Urban Fabric" Classification of scrubs and forestry regeneration Supply of images Amount of data and image processing Data types by LUZs Availability of archive images and Image quality Image data format Use of "Soil-Sealing" Abstract Assessment of users requirements Evaluation of the soils sealing layer as an input to the Urban Atlas 26 Page 2/96

3 Distribution of unfilled urban fabric polygons Conclusions Detailed description of the methodological approach NDVI calculation Image processing methodology Production flow Results Recommendations for further UA updates LUZs limits Road splitting Recommandations for future use and updates Mapping guide LUZs limits Soil Sealing Image management Quality control Internal quality controls Attribute table controls Geometric control Continuous control Photo-interpreters cross-controls External quality controls Pre-defined grid sampling Determining the number of control points Determining the distribution of control points (Secondary sampling Unit) Blind test by three European experts Quality improvement Advantages of the method Intermediate delivery control Final delivery control Quantifying accuracy : the confusion matrix Global matrices Systematic verification by EEA Content of controls Results Experts check and validation Progress of the checks Results of the expert checks Confusion matrices QA-SIRS/ Experts validation results comparison Main conclusions and experts remarks Land Cover areas statistics Data delivered 60 Page 3/96

4 6.1. For each LUZ Vector data Delivery report Land Use/cover Maps LUZs Deliveries Report 66 Conclusion 67 Appendix 1 : List of LUZs produced 70 Appendix 2 : Results of confusion matrices by LUZ 78 Appendix 3 : Land/Use Cover statistics 85 Area percentage by Country level 3 85 Area percentage by Country level 2 86 Area percentage by Country level 1 87 Appendix 4 : LUZ Delivery Report - Hamburg example 88 Appendix 5 : LUZ Map - Manchester (UK) example 95 Page 4/96

5 ILLUSTRATION TABLE Table 1 : Product accuracies 13 Table 2 : Legend table 15 Figure 1 : Application of MMU 2500 m² in BRUXELLES center 17 Table 3 : «URBAN FABRIC» Typology 17 Table 4 : Images files received ( type and number) 19 Table 5 : Data type by LUZs 20 Figure 2 : BRISTOL, First set of datas 22 Figure 3 : BRISTOL, Second set of datas 23 Figure 4 : BRISTOL, Final set of datas 23 Figure 5 : WESTON UPON MARE Pansharpening 24 Figure 6 : Kobenhaven, «Urban Fabric» existing and new soil sealing 20 Figure 7 : Urban mask results: Copenhagen (DK) 28 Figure 8 : Images used in the original Soil Sealing production 29 Figure 9 : Scatter plot(a) and retrieval of original Sealing/NDVI relation ship(b) 32 Figure 10 : Kobenhaven, «Urban Fabric» classification with existing and new soil sealing 34 Figure 11 : Stockholm, CAPI adjustments of LUZs limits 35 Figure 12 : LUZ Delivery report Topologycal quality check extract 36 Figure 13 : CLC Urban mask creation 39 Figure 14: Urban/Non Urban sample 40 Figure 15 : Example : control point of Göttingen 42 Figure 16 : Example : control point of Cambridge 43 Figure 17 : Example : control point of Lyon 42 Figure 18 : Experts Influence Areas 45 Figure 19: Brussels matrices 48 Figure 20 : Urban Atlas global matrices 50 Figure 21: Detail check list data 51 Figure 22: Detail check list metadata 52 Table 6: Expert matrices results 54 Table 7 : QA-SIRS/Validation «Experts» results comparison 55 Figure 23: Overall Accuracy 56 Figure 24 : Rural Accuracy 56 Figure 25: Urban Accuracy 57 Figure 26 : Land use/cover repartition types by countries on level 2 of the typology 59 Figure 27 : Land use/cover repartition types by countries on level 1 of the typology 60 Page 5/96

6 Document identification Document release sheet Project Title Reference Version 2.0 Date Urban Atlas Final Report - Urban Atlas Final_Report_Urban_Atlas _v1.0_ Author Distribution SIRS Lionel MEQUIGNON, Enterprise and industry directorate general, SIRS Version 2.0 Approval network Name : Jean-Paul GACHELIN C.E.O. SIRS. Date : Approval Page 6/96

7 List of abreviations ALOS AOI CAPI CLC CORINE EC EEA EO ESA ESRI EU FTS FTSP GIS GMES IFEN IGN-FI LUZ MMU MMW NDVI QA SIRS SL SPOT TB UA VHR Advanced Land Observation Satellite Area Of Interest Computer Assist Photo-interpretation Corine Land Cover COordination des INformations sur l Environnement European Environment Agency Earth Observation European Space Agency Environmental Systems Research Institute European Union Fast Track Service Fast Track Service Precursor Geographic Information System Global Monitoring for Environment and Security Institut Français de l'environnement Institut Géographique National - France International Large Urban Zone Minimal Mapping Unit Minimal Mapping Width Normalized Difference Vegetation Index Quality Assessment Systèmes d'information à Référence Spatiale Sealing Layer Système Pour l'observation de la Terre Terabyte Urban Atlas Very High Resolution Page 7/96

8 Executive summary This document presents the final report on the production of the Urban Atlas. The aim is to present the progress made by the project, its background and the difficulties encountered. This document will also provide information which will allow the product to be improved for future updates. The main difficulties encountered and the solutions/ improvements implemented fall into three categories, detailed below. - Technicals: Level of detail for taking roads into account: Should only the most important routes be considered or should a maximum number of routes be taken into account so as not to neglect the surface area covered by the road network? The second option was chosen. Complexity of the routes and associated spaces polygons: The significant geometric complexity of these objects required the polygons to be cut into a regular 10km sided grid for the largest LUZs. This complexity prevented the geometrical and topological controls from being completed (technological limits). Minimum Mapping Unit : The MMU planned for in the Mapping Guide had to be decreased. In fact, the consideration of the entire road network consequently resulted in a larger segmentation of the land use. Therefore, certain homogenous objects were cut across by the network of routes and divided into several distinct entities which were smaller than the MMU. In order to preserve them and so as not to introduce a bias into the map, it was decided that the MMU should be decreased to 500 m² instead of 2500 m² for the posts relating to artificial surfaces and to m² for the other surfaces. Quality of the Soil Sealing data: The Urban Fabric posts were classified automatically using the Soil Sealing raster data. Problems were identified regarding this data being incomplete (pixels with a 0 value while the corresponding surface had buildings on it) at the beginning of the project. This data therefore had to be re-calculated using the interpreted Urban Fabric polygons. - Thematicals: Urban Fabric land use posts. Habitat zones are mapped and classified according to the proportion of artificial surface within these zones. In the typology used at the start, the habitat zones where the proportion of the artificial surface was less than 10% were Page 8/96

9 obscured. An additional post was therefore created in order to take them into account: 11240: Discontinuous Very Low Density Urban Fabric (S.L.: <10 %). Classification of Scrub and Forest Regeneration: These types of land use were initially classified in the 20000: Agricultural, Semi natural and Wetland areas post. Following discussions, classification in the 30000: Forest post was judged to be more suitable. - Logistics: Three main difficulties were encountered. They relate exclusively to the provision of satellite images. Availability of archive images: The production of the Urban Atlas planned for images acquired in 2006 (+/- 1 year) to be used. However, it turned out that this data was either not available or it was incomplete for some of the LUZs. The acquisition of new images therefore had to be programmed for 2009 and Unfortunately, this introduced heterogeneity in processing the LUZs in terms of the dates the images were taken. Image processing: As part of the contract, the plan was for the images to be delivered ready for use. For the SPOT data this aspect of the contract was complied with; however this was not the case for the ALOS data. With regard to the latter, production team had to take charge of fusing the panchromatic data with the multispectral data. This involved a significant additional investment in terms of time on the part of production team. Images quality (e.g. clouds, snow and gaps): The technical specifications detailed that images were acceptable if the presence of clouds, snow or the absence of data was below 5% of the total surface of the LUZ. In practice, images had to be rejected even when they satisfied this condition. In actuality, in the example of mapping urban environments, it was not acceptable to use images with cloud cover over urban areas. This ratio should only apply to rural or natural areas. The consequence was numerous exchanges, research into alternative images and therefore a significant amount of time spent. Lastly, all the difficulties encountered were resolved. Lessons should be taken from this experience so that future work of a similar nature, specifically the future update of the Urban Atlas, can be carried out under the best conditions. Page 9/96

10 1. Background 1.1. Nature and purposes The Urban Atlas contract is a service contract for the delivery of land use/cover maps of major European urban agglomerations. The work consists of the production of a series of land use/cover maps of larger urban zone (LUZ) as defined in the Urban Audit identified by a list of coordinate polygons. The work shall be based on satellite images of the reference year 2006+/-1 wich will be made available by the EC through ESA. The output of the work shall be in the form of GIS compatible vector maps of the urban agglomerations mentioned above. The Urban Atlas is a joint project of the Regional Policy DG and the Enterprise and Industry DG. The project will complement the Urban Audit with harmonised data on land use and land cover. This project will provide a European overview of urban land use patterns and the possibility to track changes in the future, which so far has not been available. Currently, the only source of EU-wideland use or land cover information is CORINE which was primarily designed to measure natural areas and agricultural land. Due to the coarse resolution and high density thresholds, CORINE failed to capture large parts of urban areas. The land use maps will also be of interest to the cities concerned as they will be able to compare their land use patterns with that of other European cities. Furthermore, the Urban Atlas offers cities the opportunity to build more detail on top of these maps, the socalled "downstream services". The Urban Audit ( is a database allowing pan-european comparison of over 500 cities with a set of over 300 indicators across the domains Demography, Social Aspects, Economic Aspects, Civic Involvement, Training and Education, Environment, Travel end transport, In-formation Society, Culture and Leisure. The Urban Atlas aims to become part of a sustainable GMES Core Service that is continuously updated to find acceptance and ownership by local users, both in the public and private domain. Page 10/96

11 1.2. Related Documents Input Document ID Description CALL FOR TENDERS N ENTR/08/029 Technical specifications ITD-0421-RP-0003-C5 C5 Service Validation Protocole ITD GSELand-TN-01 Mapping Guide for a European Urban Atlas V1.1 Atlas_Urbain_v2.0_ SIRS/IGN FI/EUROTOPO-IGC proposal Offre_UA_V Verification and correction of the sealing layer before its usage in the production oflarger Urban Zone Maps Atlas_Urbain_contrat2_v1.1_ Offre renouvellement UA V QAP_V3.0_ QAP - SIRS/IGN FI/EUROTOPO IGC Output Document ID Description UA_DR_ CE - Urban Atlas - Contract 1 - Final Luz Delivery Report Luz UA_DRM_ CE - Urban Atlas - Contract 1 - Final Luz Delivery Report - Maps Luz UA_DR_ CE - Urban Atlas - Contract 2 - Final Luz Delivery Report Luz UA_DRM_ CE - Urban Atlas - Contract 2 - Final Luz Delivery Report - Maps Luz UA_Final-Report_ Urban Atlas Delivery of Land/Use maps of major European urban agglomerations_v List of LUZs produced The table of delivered LUZs is presented in appendix N 1. LUZs are classified by date of delivery. 305 LUZs have been delivered for a global area of km² and polygons created. 239 LUZs have been delivered according to the priority list of the first phase of the contract and 66 in the second phase. Nevertheless, for technical reasons related to the supply of satellite images, some LUZs first planned within contract 1 could not be produced. These LUZs have been replaced by LUZs planned within contract 2. These are LUZs of Rzeszow, Nowy Sacz, Derry and Page 11/96

12 Opole, replaced respectively by Jelenia Gora, Koblenz, Perugia and Besançon. These replacements have been made so as to respect the first planned areas to be produced. 3. Urban Atlas Methodology (extract of Mapping guide v1.1) 3.1. Pre-processing and geometric adaptation of COTS navigation data The EO data are the basis for interpretation. The geometrical differences between EO data and COTS navigation data, the COTS navigation data has to be corrected towards the EO data Pre-processing of Topographic Maps Topographic maps are used for interpretation of objects. Topographic maps should be used in digital form with precise geo-coding. The usage of printed (analogue) maps is not recommended. In case of geometrical differences between EO data and topographic maps, the erroneous data (either RS-data or topo-maps) needs to be identified using reliable datasets providing spatial reference information. Then the geometry of the mapping product shall be congruent with the correct dataset Classification and Interpretation Application of automatic classification routines, as segmentation and clustering be applied whenever appropriate: - Automated segmentation and classification to achieve a first differentiation into basic land cover classes (urban vs. forest vs. water vs. other land cover) is possible and upon decision of the service providers. - As backbone for the object geometry the COTS navigation data network is recommended but only with the method defined in the Annex of the Mapping guide Application of FTS Sealing layer The FTS - sealing layer is used for classification of the sealing densities of class 1.1 Urban fabric in level 3 and level 4. Page 12/96

13 3.5. Interpretation rules The delineation is to be done on the EO Data. EO data should be considered as primary (guiding) data source. The interpretation of the object is done using: the EO Data, the topographic maps and COTS navigation data; auxiliary information including local expertise. The interpreted area should be interpreted with at least 100m extension (100 buffer) to ensure accuracy and continuity of polygons. During the postprocessing phase a subset with the spatial extent of the final product will be generated. At the border of this subset (final product) polygons smaller than the MMU may be present. In areas where two or more scenes overlap, it must be assured, that the most recent data are used for delineation and interpretation. In case of cloud-coverage on the most recent scene the affected part (only this part!) shall be interpreted using a cloudless older scene. If two or more objects overlapping at different levels, the top level is mapped continuously, e.g. Road Bridge over Railway is mapped as seen, the railway polygon is broken and the road is mapped as a contiguous feature. In case of two or more objects overlapping on the same height level, the visually dominant and complete object (in use and shape) is mapped continuously. For example a road / railway crossing viewed at the same height level: the railway shall be mapped continuously to maintain the network. The road shall be broken. Table 1: Product accuracies Page 13/96

14 3.6. Minimum mapping unit Minimum mapping unit (MMU): - Class 1 : 0,25 ha - Class 2 5 : 1 ha Exception of MMU 0.25 / 1ha : in case of an homogeneous area > MinMU but divided in 2 or more polygons by the road network, each part can be smaller to preserve the land cover information. However, no polygon can be smaller than 500 m² (e.g.: a 1 ha forest divided in 4 polygons by the road network has to be mapped). Minimum mapping width (MMW) between 2 Objects for distinct mapping: 10m Maximum mapping width (MMW) between 2 Objects for mapping together: 10m Exception of minimum width 10 m of a mapping unit: to maintain continuity of linear structures, they can be mapped smaller than 10 m over a distance of up to 50m Priority rules Priority mapping rules for areas smaller than the MMU: Smaller areas are added to the adjacent unit with the next lesser number of the same sub-class, Smaller areas are added to the adjacent unit of the same upper class, Smaller areas are added to the adjacent unit with the longest common border line, except to railways or roads (exception here: if an object is below the MMU size and completely surrounded by e.g. road or railway network it shall be aggregated with that surrounding traffic line). Page 14/96

15 3.8. Legend Table 1 Artificial surfaces 11 Urban Fabric Table 2: Legend Table Continuous Urban Fabric (S.L.: > 80%) Discontinuous Dense Urban Fabric (S.L.: 50% - 80%) Discontinuous Medium Density Urban Fabric (S.L.: 30% - 50) Discontinuous Low Density Urban Fabric (S.L.: 10% - 30%) Discontinuous Very Low Density Urban Fabric (S.L. < 10%) Isolated Structures 12 Industrial, commercial, public, military and privates units Industrial, commercial, public, military and private unit Fast transit roads and associated land Other roads and associated land Railways and associated land Port areas Airports 13 Mine, dump and constructions sites Mineral extraction and dump sites Construction sites Land without current use 14 Artificial non-agricultural vegetated areas Green urban areas Sports and leisure facilities 2 Agricultural + Semi-natural + Wetlands areas 3 Forest (natural and plantation) 5 Water (description of mapping units can be found in the mapping guide v1.1) 4. Encountered difficulties and solutions 4.1 Adjustments to the "Mapping Guide" Interpretation rules The objects were interpretated using : - the Earth Observation data, - the Navigation data, Page 15/96

16 - ancillary information. The interpretation rules are described in the Mapping Guide (Tender Specifications v1.1) However the Mapping Guide V1.02 had to be amended early on in of the project, following the production of the first five LUZs Minimal Mapping Unit (MMU) The contractual Minimal Mapping Units of thematic extractions were 2500 m2 for nomenclature posts relating to artificial areas (classes 1 ) and m2 for the others (2, 3., 4.. and 5.). As a result of the technical choice aimed at respecting the roads in their entirety, the problem of polygon split arose. Actually, in certain cases, the density of the network caused a split of the homogeneous thematic entity into several entities whose area was under the cartographic thresholds specified by the project. Compliance with these specifications thus required to union the entities below the thresholds to be combined with the included "road" entity. This would have introduced thematic anomalies within the mapping. In order to avoid these thematic skews as much as possible, the decision was required to lower the cartographic threshold. Relative to the results from the different tests, the minimum cartographic value kept was 500m2. Thus, no polygon (except borders) must be under this threshold in the maps produced. The following presentation on a BRUSSELS extract shows the result of a dissolving polygon under 2500 m². Page 16/96

17 Figure 1 : Application of MMU 2500 m² in BRUSSELS centre The application of the rule of 2500 m² in the centre of Brussels induces the reclassification of multiple polygons "Urban Fabric" in "Roads and associated lands. The mapping is biased Mapping Guide updates : This change has been specified in the "Mapping Guide" by the addition to the section « Minimum mapping units» of the following sentences: Exception of MinMU 0.25 / 1ha : in case of an homogeneous area > MinMU but divided in 2 or more polygons by the road network, each part can be smaller to preserve the land cover information. However, no polygon can be smaller than 500 m² (e.g.: a 1 ha forest divided in 4 polygons by the road network has to be mapped) Typology : post "Urban Fabric" The typology relating to the "Urban fabric" posts, first decided upon at the start of the Urban Atlas project, was as follows: Table 4 : «URBAN FABRIC» Typology Continuous Urban fabric (S.L. > 80 %) Discontinuous Dense Urban fabric (S.L.: %) Discontinuous Medium Density Urban fabric (S.L.: %) Discontinuous Low Density Urban fabric (S.L.: %) This classification is based on the proportion of artificial area compared to the vegetated areas within a group of residential housing. It is automatically calculated by crossing it with "soil sealing" data. Page 17/96

18 This typology appeared to be incomprehensible as it did not take into consideration the urban fabric which has vegetated surface proportions of over 90%, but which cannot be classified as forests or agricultural areas. It was thus decided to create an additional post aimed at characterising these areas with a very low urban fabric density: Discontinuous Very Low Density Urban fabric (S.L.: < 10 %). Mapping Guide updates : The changes brought to the "Mapping Guide" consisted in - Integrate the post Discontinuous Very Low Density Urban fabric (S.L.: < 10 %) in the section 4.4: Legend Table, - Update the section «4.6: Description of mapping Units Urban atlas» by the addition of the following text : Discontinuous Very Low Density Urban Fabric MMU: 0,25 ha, MMW: 10 m Average degree of soil sealing: < 10% Predominant residential buildings, roads and other artificially surfaced areas. The vegetated areas are predominant, but the land is not dedicated to forestry or agriculture. Example: exclusive residential areas with large gardens Classification of scrubs and forestry regeneration. Firstly, the "mapping guide" specification was to class scrubs and forestry regeneration in the post 20000: Agricultural, Semi-natural and Wetlands areas. This choice, even if it can be justified, has been discussed by the European Experts in charge of the thematic quality control of the data produced. It was then decided that the post 30000: Forest was preferable as it was more relevant. Mapping Guide updates : The changes brought to the "Mapping Guide" consisted in: Page 18/96

19 - Remove in the section«2 d), Shrubs and / or herbaceous Vegetation incl. transitional woodland», the sentence «Forest regeneration / recolonisation: clear cuts, new forest plantations» and add it to the section «3. Forest (natural and plantation)» Supply of images The main problems encountered during the work concerned the supply of satellite images. These problems were availability of archive images, images quality or images data format. The tables below present the number of files received and their distribution by data type. The problems encountered in terms of supply and the solutions found are then presented Amount of data and image processing Table 4 : Image files received ( type and number) Product type Number of files PRODUCTS SPOT 5 PXS (2,5 meters) 608 PRODUCTS SPOT 5 P (2,5 meters) 127 PRODUCTS SPOT 5 XS (10 meters) 115 PRODUCTS ALOS P (2,5 meters) 350 PRODUCTS ALOS XS (10 meters) 167 PRODUCTS RAPIEYE XS (5 meters) 304 PRODUCTS QUICKBIRD (2,6 meters colour) 3 At the end of the project, the total number of files received was These data represent a volume of more than 2TB Page 19/96

20 One of the problems was the necessity to merge panchromatic and multispectral images. In terms of the project, this operation had to be done first and images delivered to the producer should have been already merged. In practice, all the ALOS images and some of SPOT images have been delivered unmerged. Thus, the production Team had to assume this task. This work appeared to demand a lot of extra time as the volume of images was particularly important (242 files for SPOT and 517 files for ALOS). Recommandation: As for updates to come, this operation should be either actually done first or clearly identified as an additional task in the specifications Data types by LUZs To ensure a full coverage of the 305 LUZs, a multi-sensor strategy had to be adopted. LUZs were mostly (73%) covered by SPOT 5 data (2.5 meters resolution). 13% of LUZs are fully covered by ALOS data. As for the rest of the LUZs, it was necessary to obtain heterogeneous data because the coverage was incomplete and clouds were present, sometimes both. In the case of these LUZs, problems were major and required more time. Table 5 : Data type by LUZs Data types Number of LUZs % SPOT ,79 ALOS 39 12,79 ALOS + SPOT ,89 QuickBird 1 0,33 SPOT+RapidEye 16 5,25 ALOS+RapidEye 3 0,98 SPOT+ALOS+RapidEye 3 0, Page 20/96

21 Availability of archive images and Image quality - Availability of archive images: As for some LUZs planned in contract 1, the availability of archive images dated back to 2006 (+/-1 year), the reference year for mapping, was insufficient in terms of coverage. In order to find a solution, new image acquisitions were made in 2009 and This caused discrepancies in the homogeneity of the work over the whole LUZs in terms of reference dates. On the other hand and in spite of this, the LUZs first planned in contract 1 could not be supplied and some were replaced by LUZs planned in contract 2. - Image quality: The problem of image quality was principally linked to cloud coverage. The contract specified that images where the cloud coverage did not exceed 5% of the LUZ surface would be acceptable. However, some images had to be rejected even if they complied with this threshold. Indeed, in many cases, clouds covered the urban areas making their interpretation impossible, which was not acceptable in a mapping project principally dedicated to identifying urban areas. In some cases, the delivered images did not completely cover the LUZ area and thus could not be accepted. This concerned both the Panchromatic as well as Multispectral images. Moreover, images were mainly accepted or rejected through "quicklooks" (very undersampled images). Yet, this type of pre-visualisation only allows an a priori quality judgement to be made. Only the analysis of images in true resolution allows a reliable decision to be made. This resulted in an increase of exchanges which of course then slowed down the project s progress. Example: BRISTOL LUZ case The example of the City of Bristol is a typical case which demonstrates the difficulty in obtaining images due to a non-exhaustive coverage or presence of clouds. The following illustrations show the different steps which were required in order to acquire images showing complete cloud-less coverage. Page 21/96

22 Figure 2: BRISTOL - first dataset (ALOS panchromatic acceptable quality but ALOS XS coverage incomplete and very cloudy) Page 22/96

23 Figure 3: BRISTOL -second dataset (SPOT 5 PXS Additional data, significant cloud coverage in the SW) Figure 4: BRISTOL - final dataset (Acquisition of RapidEye complete and cloud-less cover) Page 23/96

24 The following illustration shows that in Weston-Super-Mare (Bristol LUZ) area, image processing has been used whenever possible. In this precise case, we could merge a panchromatic ALOS image (2.5 meters resolution) with a RapidEye image (5 meters resolution) so as to take advantage of both the radiometric richness of RapidEye (XS) and the geometric precision of panchromatic (P) ALOS. Figure 5: WESTON-SUPER-MARE Pansharpening WESTON: first dataset WESTON: second dataset WESTON: Pansharpening Alos Panchro + RapidEye In consideration of the problem of the images acquired being incomplete and cloudy, the following alternative solutions were found (in order): a) new research into the same satellite sensor data archives or recent acquisitions (SPOT 5 / ALOS); b) research into other satellite sensors (e.g. QuickBird, Formosat, etc.); c) research into the RapidEye data available; Page 24/96

25 d) research into IMAGE 2006 CORINE LAND COVER. The RapidEye and image2006 resolution is not compliant with the technical specifications of the Urban Atlas. Thus, the following decisions were made: - RapidEye data can only be used in addition in the case of clouds, snow or gaps being present. - Image 2006 CORINE can be used in addition in the case of clouds, snow or gaps being present only in rural or natural areas. - Recommandations for future updates: In the case of cloudy archive, the mitigation below could be proposed : - Define a area of interest for each Luz, with preferably no cloud cover. This area of interest must include build-up areas. This could be automatically generated by the extraction of "build-up" polygons out of the existing and bufferized Urban Atlas so as to take in consideration the fact that urban mutations will be notably localized in immediate suburbs. This data would thus be delivered to the image supplier so as to help him in his research. - Accept 5 or 10 meters resolution images outside the area of interest, to complete the Land/Use cover map on natural or agricultural areas. - Accept that use multisensor images are necessary Image data format In some cases, the cartographic projection system of delivered images was not compliant. This required the data to be re-processed. Finally, the files delivered were sometimes corrupted and thus unusable (archive files ".zip") The coordination tasks involved in supplying the images were significantly increased because of an inadequate quality check of the images provided by the image supplier 4.3. Use of "Soil-Sealing" Abstract An initial Soil Sealing layer was produced in 2008 based on Image 2006 data for 38 countries including the EU 27. The production of this initial 2006 GMES Fast Track Page 25/96

26 Service Precursor (FTSP) on Land Monitoring, specifically the degree of soil sealing, was based on the derivation of a built-up mask, as defined above, based on a hybrid ISODATA/Maximum Likelihood classification with manual corrections. The derivation of a sealing level within the built-up mask was based on NDVI derived from Image 2006 and visually interpreted calibration sites. This was used to derive a linear regression model of the degree of sealing ensuring that the highest NDVI value for unvegetated surfaces was set to 100 and the smallest NDVI value for an area fully covered by vegetation was set to 0. Therefore, the FTSP2006 degree of the sealing layer was based on vegetation activity within a land cover based built-up mask (i.e. an airport runway may be included, but not the grassland around it) excluding mines, quarries or construction sites that are unsealed and generally un-vegetated. It was shown very early on in the project the fact that the FTSP2006 built-up mask was based on land cover as opposed to land use would pose a problem for the UA production. Production Team has made an evaluation of the sealing layer as an input to the Urban Atlas: 40 to 60% of the artificial areas as mapped in the Urban Atlas (UA) were not classified as artificial in the Sealing layer (SL). For an accurate characterization of the UA urban fabric classes, Production Team has to fill the missing SL data for the area covered by urban fabric polygons. The proposed production methodology to update the soil sealing layer in the Urban Fabric interpreted polygons makes it possible to retrieve the original NDVI/SL relationship thus ensuring full compatibility with the original SL layer Assessment of users requirements Based on the Urban Atlas production experience, it was rapidly identified that errors of omission were present in the soil sealing layer. As a result, a quantitative assessment of the soil sealing layer as in input to the Urban Atlas was undertaken by Production Team. This detailed study provides a good understanding of omission / commission errors to be corrected Evaluation of the soils sealing layer as an input to the Urban Atlas Production Team is committed to carry out the production of the Urban Atlas. This section describes the evaluation of the soils sealing layers as an input to the Urban Atlas. Page 26/96

27 Objectives The objectives were to assess the quality of the Soil sealing on the 5 first LUZs produced: Graz (AT), Copenhagen (DK), Patra, (GR), Miskolc (HU) and Rotterdam (NL), and more specifically: Assess the potential of using the Soil Sealing layer to determine the Urban Atlas urban mask Identify any issues related to the use of the Soil Sealing layer as an input to determining the urban fabric classes Urban Mask Comparison: Soil Sealing and Urban Atlas To support Computer Assisted Photo-Interpretation (CAPI), the soil sealing layer could be used to create the UA urban mask. However, for the first 5 LUZ, the UA urban mask was deliberately created independently. The Soil Sealing layer was reclassified and the UA vector layer rasterised based on the urban vs urban codes The two masks were compared based on a sample of 1000 points selected randomly with a minimum distance of 100m apart to avoid spatial autocorrelation Results are presented in the tables below for Copenhagen (DK). Page 27/96

28 Figure 6: Urban Mask Results: Copenhagen (DK) Sample Sealing Urban atlas Sample Sealing Urban Atlas Natural Artificial Sample point Urban Atlas FTSP Natural Artificial Total User Commission Natural ,0% 29,0% Artificial ,0% 2,0% Total Grand Total 1000 Producer 98,8% 58,3% Overall 78,9% Omission 1,2% 41,7% Initial conclusions High level of omission error: 40 to 60% of the artificial areas as mapped in the Urban Atlas were not classified as artificial in the Soil Sealing layer, This makes it unpractical to use the soil sealing to automatically extract the urban mask as part of the Urban Atlas production, Page 28/96

29 Distribution of unfilled urban fabric polygons Figure 7 : Distribution of unfilled Urban Fabric polygons: Copenhagen (DK) Conclusions From a land use point of view, artificial areas can include large amounts of vegetated areas. This mean that a significant proportion of the areas classified in the Urban Fabric category were not covered by the FTSP2006 Sealing layer, making the derivation of level 3 Urban Fabric classes impossible. Tests were carried out over the 5 first LUZs produced and brought the following conclusions: The soils sealing layer cannot be used to determine the Urban Mask (omission error from 40 to 60%). The settlement extraction for the Urban Atlas production has to be done independently, 30 to 50 % of urban fabric areas not covered by soil sealing, Only 10 to 20 % of urban fabric polygons are fully covered, This could result in a shift down to one class level in the UA, e.g. polygon labelled as when in fact it should be For an accurate classification of the urban fabric classes, Production Team has to fill the gaps of the soil sealing layer on the areas covered by urban fabric polygons. Page 29/96

30 As a result of these observations, the following workarounds were: - Continue to characterise the urban mask independently from the soil sealing layer; - Assume that 0 in the soil sealing layer corresponds to a 0 sealing level (as done for the first 5 LUZ); - Include a new UA field indicating the level of soil sealing layer coverage for each polygon; - Alternatively, calculate the degree of sealing for the areas classified as Urban Fabric as part of the UA production based on Image2006 used to produce the initial soil sealing. This last solution was selected and it was thus necessary to re-calculate the soil sealing in the urban mask provided by the interpretation Detailed description of the methodological approach NDVI calculation In the existing sealing layer, it is understood that the sealing level was based on the Normalised Difference Vegetation Index (NDVI) combined with an accurate delineation of the built-up area. The relationship NDVI / sealing level is linear. NDVI = (XS3-XS2) / (XS3+XS2) It has been demonstrated in numerous past studies that the NDVI can be used as a reliable indicator of soil sealing as it assumes that any soil that is not covered by vegetation in built-up is likely to be sealed Image processing methodology The image processing methodology was tested on the Copenhagen LUZ (below) Sealing calculation The Urban Atlas vector file was used to create an Urban Mask making sure that the sealing level is derived for all artificial areas and avoid commission errors The soil sealing metadata vector file on image deviations (see figure 8 below) was used to identify the images used for the original production of the sealing layer. Care was taken to identify the right images to maximize the level of compatibility. Page 30/96

31 NDVI images were computed from raw images as a basis for the sealing level extraction. Figure 8: Images used in the original Soil Sealing production A systematic grid of points was then used to extract pixel values from both the NDVI images and the original sealing layer. NDVI and sealing observations were paired for each image to identify the images actually used in the sealing level calculation. A scatter plot was produced for each NDVI image as shown in Figure 9a below. There is a considerable scatter of points as the whole image was used to ensure no data was left out. However, the linear relationship between the sealing level and the NDVI can be clearly seen. This was used to retrieve the linear regression model used in the original soil sealing layer production as shown as Figure 9b. Page 31/96

32 Figure 9: Scatter plot (a) and retrieval of original Sealing/NDVI relationship (b) (a) (b) The linear regression model retrieved makes it then possible to recalculate the sealing level for the NDVI images used to calculate the original sealing layer and expand it to artificial areas omitted from the original sealing layer. Page 32/96

33 Updating of the existing sealing layer Only the pixels recalculated with new values are corrected in the existing national mosaics: - Original pixel value = 0 or pixel is selected as CLC 1.3.x classes - New pixel value = A conditional statement based algorithm is used to integrate the new pixels from the processed scene into the national mosaic. This will ensure that consistency problems across images are kept to a minimum Production flow Images : download and NDVI processing The image 2006 layer has to be downloaded in our Urban Atlas production chain to integrate new imperviousness calculation on the urban fabric polygons. NDVI are simultaneously calculated on the images Processing of the LUZ The production is organized on a LUZ per LUZ basis Results The following presentation shows the results on a Copenhagen LUZ extract. Page 33/96

34 Figure 10: Copenhagen - existing Urban Fabric and new soil sealing Existing Soil Sealing New soil sealing Figure 7: Copenhagen - Urban Fabric classification with existing and new soil sealing The dark red colour corresponds to the zones where the artificial surface reaches its peak; the orange and yellow correspond to the low-artificial zones. We can easily see the changes of classes after soil sealing re-calculation. Page 34/96

35 Recommendations for further UA updates In any case, the issue regarding the definition of land cover versus land use of built-up areas remains and it is strongly advisable that the sealing layer should be recalculated following the methodology developed in future updates of the Urban Atlas for the area classified as urban fabric to ensure that there is no gap in the coverage. The method is now fully automated and provides consistent results which are compatible with the original sealing layer data, as the same regression relationships are used LUZs limits In some cases, LUZs geometric limits were particularly inaccurate. This was certainly due to an inadequate choice of scale during the capture. It was thus necessary to adjust these limits in CAPI. The following image gives an example of limit adjustments made to the Stockholm LUZ (yellow: the original limits; green: adjusted limits) Figure 11 : Stockholm, CAPI adjustments of LUZs limits The new LUZ limits are available as a shape file in each Geodatabase (see section page 60). In future updates, these files of new contours to be used. Page 35/96

36 4.5. Road splitting However, in some cases, especially in huge LUZ, the geometric check and arcgis s topology failed. The main cause was the size and shape of a very large and complex polygon composed of roads (code 12220). So, to avoid this problem, the Production Team created a 10 km size grid for those LUZs, which split this huge polygon into smaller ones. Only the code roads are split using this method. After this operation, the geometric and topology checks passed successfully. When roads are split on an LUZ this is specified in the "LUZ Delivery Report" under "Topological QUALITY CHECK" section. Internal quality check Figure 12 LUZ Delivery report Topological quality check extract Date of control (y/m/d) Result Remark (errors, corrections, etc.) SIRS 11/10/04 ok Roads had been split 4.6. Recommandations for future use and updates Mapping guide The main adjustments required were made at the completion of the Urban Atlas. These have been identified early in the project. In the next update, it is recomanded to implement an operational test phase of production. During this testing phase the necessary other changes will eventually be identified LUZs limits During the production some imperfections on the limits of LUZs were identified. They were corrected especially if some surfaces were forgotten (especially on the coasts). The new limits LUZs are provided with the data produced. The scale of creation of these contours seems inappropriately and too imprecise. It may be helpful to correct them exhaustively to correspond better to the administrative reality, bio-physical,... of the territories. Page 36/96

37 Soil Sealing It was shown very early on in the project that the FTSP2006 built-up mask was based on land cover as opposed to land use would pose a problem for the UA production. The production team has made an evaluation of the sealing layer as an input to the Urban Atlas: 40 to 60% of the artificial areas as mapped in the Urban Atlas (UA) were not classified as artificial in the Sealing layer (SL). In any case, the issue regarding the definition of land cover versus land use of built-up areas remains and it is strongly advisable that the sealing layer should be recalculated following the methodology developed in future updates of the Urban Atlas for the area classified as urban fabric to ensure that there is no gap in the coverage. The method is now fully automated and provides consistent results which are compatible with the original sealing layer data, as the same regression relationships are used Image management The main problems encountered during the work concerned the supply of satellite images. These problems were : availability of archive images, images quality or images data format. In the case of cloudy archive, the mitigation below could be proposed : - Define an area of interest for each Luz, with preferably no cloud cover. This area of interest must include build-up areas. This could be automatically generated by the extraction of "build-up" polygons out of the existing and bufferized Urban Atlas so as to take in consideration the fact that urban mutations will be notably localized in immediate suburbs. This data would thus be delivered to the image supplier so as to help him in his research. - Accept 5 or 10 meters resolution images outside the area of interest, to complete the Land/Use cover map on natural or agricultural areas. - Accept that use multisensor images are necessary. 5. Quality control Four different levels of quality controls have been carried before delivery of the products to the user: - Internal quality control during production, - Independent thematic validation before delivery, Page 37/96

38 - Systematic vérification by EEA, - Sample thematic validation by external experts Internal quality controls Attribute table controls The consistency of attributes tables is automatically checked Geometric control The database geometric precision has been checked at regular intervals before each delivery Continuous control Technical details and the accuracy of the image capture are described in the Mapping Guide and its appendix. The person in charge of quality checked the accuracy of the capture. The sectorization must respect the specifications. Whenever a gap was notified, the operator had to re-do the work Photo-interpreters cross-controls The visual assessment consisted of a "cross control". At regular intervals and after a significant are had been processed, each operator submitted his work to another for control. In the end, this process ensured high quality levels and a homogeneous analysis. It also prevented results differing from one operator to another External quality controls External quality control was carried-out by IGN-FI. It consisted of a visual interpretation of a sample of points Pre-defined grid sampling The method used was a random oriented sampling according to a European grid and a variable sample step, depending on the LUZ size. The sampling was obtained as followed: - The "European" grid is the one the European Environment Agency (EEA) use for the assessment of landscapes and ecosystems. This grid is made up of a geographical Page 38/96

39 reference, orthogonal references with regular areas of one square kilometer and is refined according to the LUZ size which intersects it (whole, half, quarter) so as to control +/-10 of produced area. - A first classification in (U-UN) Urban-Non Urban is established from FTS data and CORINE Land cover level 1 - class 1 for Urban, and the grouping of the other classes level 1 for Non-Urban. Figure 13 : CLC Urban mask creation LUZ Lyon CORINE Land Cover 2000 (Level 1) CLC Urban mask This first classification is interesting as it determines the choice of a larger number of samples in urbanized areas than in non-urbanized areas. By crossing the European grid above defined with Urban-Non Urban classification, it is suggested that 80% samples in the Urban zone and 20% in the Non-Urban zone (0-25% urban) are chosen. Page 39/96

40 Figure 14 : Urban/Non Urban sample Lyon LUZ Classification Urban Non Urban + European Grid CLC Urban mask + European Grid + Sample (blue) It is proposed to consider the threshold of 33% urban per cell, the urban percentage in the meaning of CLC to define a cell as urban cells. The remaining cells will be considered by rural cells. The samples are set by the experts responsible for evaluating the quality and are not communicated to the team in charge of the production. That way, the independence of the method is guaranteed. In the overall LUZ and using the previous hypothesis (33% urban per cell), approximately 72,000 cells are defined as "urban cells" of a total of 636,000 cells intersected by LUZ and the kilometer grid. Then we obtain a ratio of 11.3% of urban cells but very close to 13% corresponding to the class 1 in CLC2000 in Europe Determining the number of control points Based on the distribution of control points required (aim at 80% of the control points for the urban cells and 20% for rural cells), it is possible to determine the number of control points to consider only the ratio determined above, that is to say11.3% of urban cells ( 1 urban cells to 8 rural cells on average). The LUZ have been classified into 3 categories according to the percentage of urban cells: Page 40/96

41 LUZ Percentage of «urbain cells» / LUZ Number of LUZ concerned weakly urbanized between 0 to 6% 81 moderately urbanized between 6 to 17% 146 Highly urbanized beyond 17% 78 From there, the average ratio of 1 urban cell to 8 rural cells can be adapted LUZ adapted To 10 rural cells Per urban cell Ratio weakly urbanized 1/100 1 point 10 points moderately urbanized 1/30 1 point 3 points Highly urbanized 1/20 1 point 2 points Whether LUZ weakly urbanized: 1 point every 12 «rural cells», 8 points by «urban cells» LUZ moderately urbanized: 1 point every 12 «rural cells», 3 points by «urban cells» LUZ Highly urbanized: 1 point every 10 «rural cells», 1,5 points by «urban cells» Determining the distribution of control points (Secondary sampling Unit) A second grid level has been defined with a smaller size 200m * 200m. 200m Cell 1 km x1 km The control points are considered as the centroids of the cells of level 2. For each of the urban categories specified in the LUZ (weakly, moderately or highly) the proposed distribution is shown in the following diagrams: Page 41/96

42 LUZ weakly urbanized: Figure 15: Example: Control point of Göttingen (red color: Urban point / Green color: Rural point) Urban Cells: Rural cells : every 10 cells of level 1 Page 42/96

43 LUZ moderately urbanized : Figure 16: Example: Control point of Cambridge (red color: Urban point / Green color: Rural point) Urban cells: Rural cells : every 10 cells of level 1 Page 43/96

44 LUZ highly urbanized : Figure 17: Example: Control point of Lyon (red color: Urban point / Green color: Rural point) Urban cells: Rural cells : every 10 cells of level Blind test by three European experts Whenever the Urban Atlas is completed for one LUZ, samples are set and three experts share samples of produced data. They operate over geographic zones where they have significant experience. Page 44/96

45 That way, the three appointed experts share their knowledge of land use across Europe. The quality of each LUZ would then be assessed by at least two experts. Figure 18 : Experts Influence Areas Expert Influence Area Quality evaluation 1 Expert Influence Area Quality evaluation 2 Expert Influence Area Quality evaluation Quality improvement The quality is estimated for each LUZ. Metadata over quality are generated and a return to production for corrections is made if the level of quality does not answer the specifications. Each class which quality is below the required thresholds is being verified by the person in charge for the thematic quality production team and the data is subjected again to external quality control. 58 of 305 LUZs have required corrections on some classes after quality control. In practice, no LUZ needed a second correction procedure. Page 45/96

46 Advantages of the method The quality assessment is independently carried out and is not subject to any interference from production teams. It is thus an external assessment which approves/rejects the data produced. This method guarantees the respect of the criteria enacted by referenced terms. The grid used by the EEA comes from the sharing of existing tools which have been tried-and-tested in control processes and in the validation of the European land use database. This grid provides a unique identifier and thus makes it easy to track samples in accordance with EEA standards. To sum up, this method is easily applicable and highly automated in terms of sample generation and the quality assessment by independent experts who have regional experience and can ensure the criteria are met Intermediate delivery control Whenever an intermediate delivery was about to be made, a complete structural and attributary quality control of the data was carried out. Quality indicators are: Final delivery control intrinsic quality of data (e.g. topology, structuration, etc.); exhaustive data (i.e. no missed areas); thematic (i.e. the analysis and interpretation are ok) Quantifying accuracy : the confusion matrix According to the specifications, the statistical accuracy of the database was assessed through the calculation of a confusion matrix relating to the quality control points created by European experts from IGN-FI. The size of this sample was decided upon in collaboration with the client. Each cell of this dual-entry chart gives the classified number of points with the: line: the point attributed by the interpreter when mapping; Page 46/96

47 column: the attribution as it was recognised using exogenous documents (i.e. aerial photographs, site visits, other databases, etc.) during the checks. The marginal and diagonal totals of this matrix are used to assess two types of accuracy, commonly called user accuracy and producer accuracy. Measurements are given by attribution categories. As for each of the LUZ produced, three confusion matrixes have been calculated. The first one focused on urbanized areas, the second on non urbanized areas and the third matrix gives an overall accuracy. As an example, please see the matrix relating to the Brussels LUZ below. Page 47/96

48 Figure 19 : Brussels matrices Urban confusion matrix (Brussels) Rural confusion matrix (Brussels) Overall confusion matrix (Brussels) Page 48/96

49 On no account could the global accuracy of each matrix be under 85%. Otherwise, the quality controller in charge of the LUZ specified the thematic problems to the operator who then had to correct the thematic discrepancies over the whole LUZ. In the end, the matrices were recalculated until the LUZ became compliant Global matrices The global matrices calculating all of the 305 delivered LUZs are presented below. In addition, a summary chart of the results obtained for each LUZ is available in the appendix. Page 49/96

50 Figure 20 : Urban Atlas global matrices Urban global confusion matrix Rural global confusion matrix Overall global confusion matrix Page 50/96

51 5.3. Systematic verification by EEA Content of controls Each delivered LUZ was systematically controlled by the EEA and the results were transcribed through a report. This Database Technical Acceptance (DBTA) report summarizes the results of the technical acceptance procedure done for a final Urban Atlas database according to the standard check list defined. Technical quality has been checked for data and metadata provided in the delivery. If errors are detected, the report is transmitted to the producer who corrects and makes a new delivery. The following figures shows the various controls. Figure 21: Detail check list - Data Page 51/96

52 Figure 22: Detail check list - Metadata Results Generally, the percentage of LUZs for which the control showed errors is low (- 5%). Most of them concerned LUZs delivered earlier in the project. The main reason is that initially the LUZs were delivered to the shape file format. When deliveries have been made in Geodatabase, format releases for non-compliance were exceptional. In addition, a rigorous topological and structural quality control procedure has been established (see Appendix 4 page 94) Experts check and validation In the first and second contracts, a validation session was organised by the European Commission Progress of the checks The control took place in the producer premises in October 2009 and February 2011 and was globally done according to the same procedure as the external control described above. Page 52/96

53 Eight approved experts in Land Cover Mapping were appointed by the client (the ) and the 21 following LUZs were assessed, for a total area of around km 2 (13 % of the total LUZ area): - Lyon (FR) - Kaunas (LT) - Iraklion (GR) - Warzawa (PL) - Lisbon (PT) - Tampere (FI) - Bruxelles (BE) - Craiova (RO) - Kopenhavn (DK) - Valetta (MT) - Berlin (DE) - London (UK) - Tartu (EE) - Burgas (BG) - Gyor(HG) - Roma (IT) - Wien (AT) - Zilina (SK) - Madrid (ES) - Arnhem (NL) - Linkoping (SE) For each LUZ, a grid of points was generated in accordance with the same principle adopted for the external quality controls made up by IGN-FI. The only difference was that the controls had to be carried out using two procedures: a blind control (the experts did not have the producer s interpretation) and a non-blind control (the experts had the producer s interpretation). As a result of these control procedures, the confusion matrices were calculated in accordance with the same principle as described above (3 matrices per LUZ) Results of the expert checks Confusion matrices The overall accuracies resulting from the experts controls are presented below. Page 53/96

54 LUZ Sampling points Overall accuracy Rural accuracy Urban accuracy Urban Atlas We can see that overall the results meet the specifications of the "validation protocol". Table 6: Expert matrices results (blind re-interpretation) Bruxelles ,77% 94,76% 91,27% Kobenhavn ,76% 95,71% 87,01% Valetta ,96% 94,78% 85,96% Warszawa ,29% 95,21% 92,39% Lisboa ,92% 96,45% 92,76% Kaunas ,13% 96,06% 93,18% Tempere ,32% 95,21% 92,62% Lyon ,97% 96,99% 94,79% Iraklion ,24% 96,75% 90,36% Craiova ,57% 94,96% 90,29% Berlin ,04% 87,41% 87,41% Burgas ,13% 99,79% 95,54% Gyor ,88% 98,43% 99,43% London ,20% 93,40% 95,00% Madrid ,00% 86,65% 94,09% Roma ,88% 97,76% 97,99% Tartu ,75% 98,20% 94,35% Wien ,10% 96,65% 97,67% Zilina ,92% 95,04% 94,75% Arnhem ,98% 92,25% 93,75% Linkoping ,64% 91,52% 96,78% QA-SIRS/ Experts validation results comparison The following chart and illustration shows a comparison of the results from both of the quality controls carried out on data of the 21 produced LUZs. Page 54/96

55 LUZ Overall accuracy "Validation" Overall accuracy "SIRS internal QA" Rural accuracy "Validation" Rural accuracy "SIRS internal QA" Urban accuracy "Validation" Urban accuracy "SIRS internal QA" Urban Atlas Table 7: QA-SIRS/Experts validation results comparison (blind reinterpretation) Bruxelles 90,77% 88,40% 94,76% 93,10% 91,27% 89,40% Kobenhavn 86,76% 95,70% 95,71% 98,10% 87,01% 95,90% Valetta 85,96% 91,10% 94,78% 96,40% 85,96% 91,10% Warszawa 91,29% 85,20% 95,21% 88,90% 92,39% 85,40% Lisboa 90,92% 85,20% 96,45% 91,30% 92,76% 85,50% Kaunas 92,13% 85,00% 96,06% 90,60% 93,18% 85,00% Tempere 91,32% 87,50% 95,21% 93,80% 92,62% 88,50% Lyon 93,97% 91,70% 96,99% 94,60% 94,79% 93,90% Iraklion 90,24% 96,30% 96,75% 98,10% 90,36% 96,30% Craiova 89,57% 85,30% 94,96% 89,30% 90,29% 85,30% Berlin 91,04% 87,00% 87,41% 95,00% 87,41% 86,20% Burgas 98,13% 87,50% 99,79% 89,10% 95,54% 88,50% Gyor 98,88% 86,00% 98,43% 86,50% 99,43% 88,60% London 94,20% 87,50% 93,40% 92,90% 95,00% 86,90% Madrid 90,00% 85,40% 86,65% 91,70% 94,09% 86,00% Roma 97,88% 85,30% 97,76% 91,90% 97,99% 86,60% Tartu 96,75% 91,50% 98,20% 91,70% 94,35% 86,90% Wien 97,10% 87,70% 96,65% 95,00% 97,67% 88,00% Zilina 94,92% 90,20% 95,04% 94,00% 94,75% 92,10% Arnhem 92,98% 90,50% 92,25% 95,80% 93,75% 90,50% Linkoping 93,64% 91,10% 91,52% 95,20% 96,78% 93,40% Page 55/96

56 Figure 23 : Overall Accuracy Figure 24: Rural Accuracy Page 56/96

57 Figure 25:Urban Accuracy The compared results of both controls (QA-SIRS and expert validation ) confirm the legitimacy of the internal QA/QC results made up by the IGN-FI controllers throughout the LUZ production Main conclusions and experts remarks Although the results of the experts controls appear to be satisfactory and the work carried out by the producer has been qualified as "good", several remarks aimed at further improving the quality of the data produced have been expressed: - In the case of scrubs and forestry regeneration: as mentioned in paragraph "3.2.3", the "mapping guide" first specified the classification of scrubs and forestry regeneration into the post 20000: Agricultural, Semi-natural and Wetland areas. This choice, even if justifiable, was discussed by the European experts who decided to that the post 30000: Forest was preferable as it was more relevant. - Density of road network: The choice to represent the whole of the road network sometimes led to an over density which may make the reading of the map uncomfortable, particularly for the city centre areas. In rural areas, the problem lies in the representation of private lanes and agricultural lanes as roads when they are not. Page 57/96

58 However, this exhaustive representation also ensures that an exaggerated bias is not introduced in terms of under-estimating the artificial areas where the networks belong (an important aspect within the Urban Atlas). On the other hand, a choice orientated towards an under-representation could also introduce a lack in terms of the presence of substructures according to the different types of landscapes existing within the European Union. The decision taken at the beginning of the project was not discussed. However, as the production went on, photo interpreters tried as far as possible to remove lanes in the landscape that were not considered to be roads. - The case of the "Urban Fabric" post: During their validation session, the experts did not have access to the data re-generated after the soil sealing corrections. They were therefore unable to consider the different levels of density of the residential urbanised areas. A remark has been made concerning the presence of distinct density within globalized polygons. This remark has been taken into consideration and it was decided to implement an extra phase of re-cutting the polygons encoded in "11" before processing using soil sealing in accordance with the contained densities. - Green urban areas and forest: the distinction of forest (i.e. class 30000) from green urban areas proved to be difficult, e.g. when wood areas are rather large and located within the scattered urban fabric or the forest area obviously contains recreational elements, but is rather large and not fully surrounded by urban fabric. Unfortunately, this classification also depends on the subjectivity of the photo-interpretation. In order to ensure homogeneous processing, wooded areas in urbanised and suburbanised areas were systematically checked by the person in charge of the quality control before completion and data delivery. Page 58/96

59 5.5. Land Cover areas statistics Statistics on the surfaces of each type of land use (level detail, level 2 and 1) are collated in appendix 3. They show the major trends in the distribution of land cover classification for each class for each country and highlight the significant differences between different countries. Figure 26: Types of land use/cover distribution by country on level 2 of the typology 100% 80% 60% 40% % 0% BE MT NL PT UK HU FR IT RO DK DE GR AT IE FI PL SL CZ LU CY SK LT ES BG SE EE LV Page 59/96

60 Figure 27: Types of land use/cover distribution by country on level 1 of the typology 100% 80% 60% 40% % 0% BE NL UK FR DE DK AT FI CZ PL BG LT SE EE 6. Data delivered At the end of the project the following data were delivered: 6.1. For each LUZ Vector data Data were delivered in ESRI Geodatabase v9.3 format. The data were delivered in advance processed by the cell quality and GIS systems to ensure full consistency and structural topology. Page 60/96

61 Data structure. Geodatabase naming is standardised. In this example, the geodatabase is named de002l_hamburg.gdb. Both characters indicate the country (e.g. DE ); The four characters below show the serial number of the LUZ in the country (e.g. 002l ); A variable number of characters preceded by an underscore to the name of the city (e.g. Koln ). In addition, specific characters are avoided (e.g. Koln and not Köln ). > Within the geodatabase are: Page 61/96

62 The file "check geometry", testing inconsistencies in the geometry (an empty file by definition). A set of geodatabase feature classes with the English name of LUZ. The set of feature classes. Feature classes are located in the following set of classes of entities: > The «contour» class: This class contains the outline of the LUZ. Page 62/96

63 The attributes of this class are: CITIES: Attribute type text, 254 characters. LUZ Name (e.g. Hamburg); LUZ_OR_CIT: Attribute type text, 254 characters of the ID LUZ (e.g. DE002L). The XXYYYY_city class (uses the same naming rules as those established for the naming of the geodatabase). The attributes of this class are: CITIES: Attribute type text, 254 characters. LUZ Name (e.g. Hamburg); LUZ_OR_CIT: Attribute type text, 254 characters of the ID LUZ (e.g. DE002L); Code: Type attribute text, 7 characters. Code identifying the land (e.g ); ITEM: Type attribute text: 150 characters. Land in the clear (e.g. bodies of water); PROD_DATE: text attributes: 4 letters: Year the data was produced. Page 63/96

64 > The routes class includes the polygons of roads. The attributes of this class are: CITIES: Attribute type text, 254 characters. LUZ Name (e.g. Hamburg); LUZ_OR_CIT: Attribute type text, 254 characters of the ID LUZ (e.g. DE002L); Code: Type attribute text, 7 characters. Code identifying the land (e.g ); ITEM: Type attribute text: 150 characters. Land in the clear (i.e. other roads and associated land) PROD_DATE: text attributes: 4 letters: Year the data was produced. Page 64/96

65 > Metadata Metadata were produced using the EEA s "Metadata editor". The standard used is the EEA-MSGI/ISO > The reference system is WGS84 UTM zone (e.g. WGS_1984_UTM_Zone_32N) Delivery report Using the Geodatabase a LUZ Delivery Report was delivered. This document is an external metadata and contains any relevant information about the production of the LUZ (e.g. projection, images used, photointerpreters remarks, quality control results, etc.). An example of the metadata file delivered for Hamburg (DE) is presented in the appendix 4 (page 88). Page 65/96

66 Land Use/cover Maps For each LUZ, an A3 map was produced. The files are in "PDF" format. An example of the LUZ of Manchester (UK) is presented in the appendix 5 (page 95) LUZs Deliveries Report The "Final LUZ Deliveries Report" is a compilation of reports on the production of each LUZ (Delivery Report). A document is delivered for each of the two contracts. Page 66/96

67 Conclusion Overall, the project had proceeded satisfactorily. The feedback received from the users seems to show real satisfaction from the point of view of the usefulness of the data and from the point of view of its quality. Early on in the project and following the test production of 5 LUZs, it was considered as necessary to adjust some of the methodological points. These are already described in Chapter 3 of this report. In summary, these adjustments concerned: - adapting the "Mapping Guide" (MMU, "Urban Fabric" typology post, classification of Scrubs and Forestry regenerations); - the need to complete the FTSP2006 which presented gaps and made it impossible for a correct classification of the "Urban Fabric" items; - the need to correct the contours of some LUZs; - the requirement to cut routes because of the complexity of the polygons for the largest LUZs. Once the necessary adjustments were made, the production did not meet with any other major technical problems except for the supply of images (see below). Based on the experience gained in producing Urban Atlas 2006, the following thematic points are suggested from the producer point of view to be considered in a view of a further effort aimed at updating the 2006 database: 1. Better compatibility between Urban Atlas and Corine Land Cover Considering the current definition of the classification schemes, UA and CLC are suitably compatible in the urban zones. However, such compatibility is no longer assured in the rural areas of the LUZs. In fact, in UA the 1ha classes are represented as Agriculture (2000, including wetlands and semi-natural areas), Forest (3000) and Water (5000) whereas CLC classifies wetlands in a specific separate class (4000) while the seminatural areas are included in the class Forest (3000) instead of Agriculture (2000) as is the current case for UA. In order to assure a more encompassing compatibility between the two datasets, it is therefore suggested to slightly modify the current Urban Atlas classification scheme in the rural areas (1ha classes) according to the CLC classification scheme.that would imply at the start of the Urban Atlas 2012 campaign the requirement to consider some additional processing time necessary to update UA 2006 according to the new classes. 2. Soil Sealing Layer integration in Urban Atlas (Urban Fabric classes) Page 67/96

68 The Soil Sealing Layer 2012, to be produced by the recently announced GIO HRL effort, and used for updating the Urban Fabric classes in Urban Atlas 2012, will require (as it was the case for Urban Atlas 2006), some additional processing time so that to make it compatible to UA due to some inconsistencies between the two datasets. As explained earlier in this report, these inconsistencies are due to the fact that those datasets are based on a different spatial structure (spatial resolution) but also on a different conceptual framework with the Soil Sealing Layer (or Imperviousness Layer) being a classical land cover product while Urban Atlas derived as result of mixture of land cover and land use analysis. 3. Allow flexibility in determining the suitable methodological approach (categorisation). According to the practical experience obtained in the production of Urban Atlas 2006, the quality of the different satellite imagery (frames) used for the interpretation/classification of the different LUZs (and, sometimes, within the same LUZ) is rather variable, highly depending on various and specific situation (such as clouds and other meteorological conditions affecting the image, or the time of image capture and issues related to sun declinations and shadows, or sensor inclinations differing to nadir acquisitions, etc). As a consequence of all these factors affecting the data captured, it is consider important to guarantee certain flexibility in the approach to be adopted for feature determination. In particular, it is strongly advised not to impose, a priori monolithical specific methodology (ie: automatic classification vs visual photo-interpretation) but to allow flexible adaptation of the methodology regarded by the producer as more appropriated according to the specific situation. 4. Product validation It is suggested to adopt a system based on good practices in order to define a precise methodology for evaluating the accuracy in features classification. Concerning the imagery, the main real difficulty encountered during the project implementation concerned the supply of images due to them being either insufficient in terms of area coverage or to the presence of clouds. In order to envisage possible solutions that can be used to minimize these kind of problems for future efforts, we suggest that: - each image Provider adapts their image QC based to the Urban Atlas specifications; - images outside the specification in terms of cloud cover to be additionally delivered since they could supplement the images already selected that are regarded insufficient to suitably cover the entire AOI; Page 68/96

69 - widen the choice of images (same sensors) by agreeing to use older images (3 years) or more recent ones (only one year); - widen the choice of sensors and produce composite images on the cloudy areas, or use images from lower resolution sensors like Rapid Eye, IRS, etc. In the future, especially with regard to the future update of the Urban Atlas, the difficulties encountered and the risks they represent should be considered from the start of the project. Overall, the project had proceeded satisfactorily. The feedback received from users seems to show real satisfaction from the point of view of the usefulness of the data and from the point of view of its quality. Page 69/96

70 N ON ORDER LUZ COUNTRY SURFACE OF LUZ (KM²) START DATE END DATE Internal Quality Control Quality Assessment Final Quality Control DATE OF DELIVERY CONTRACT NUMBER Urban Atlas Appendix 1 : List of LUZs produced CAPI / DIGITAL CLASSIFICATION DELIVERY 1 Bruxelles BE 1639,73 km² 25/02/ /03/2009 OK OK OK 04/12/ Praha CZ 7008,19 km² 03/09/ /10/2009 OK OK OK 04/12/ Kobenhavn DK 2837,98 km² 19/02/ /03/2009 OK OK OK 04/12/ Tallinn EE 4350,10 km² 13/05/ /06/2009 OK OK OK 04/12/ Roma IT 3616,84 km² 16/03/ /04/2009 OK OK OK 04/12/ Lefkosia CY 2724,32 km² 07/05/ /05/2009 OK OK OK 04/12/ Vilnius LT 4266,07 km² 02/06/ /09/2009 OK OK OK 04/12/ Luxembourg LU 2612,43 km² 15/05/ /06/2009 OK OK OK 04/12/ Budapest HU 2536,07 km² 11/05/ /05/2009 OK OK OK 04/12/ Valetta MT 243,61 km² 06/05/ /05/2009 OK OK OK 04/12/ Amsterdam NL 1183,76 km² 31/03/ /04/2009 OK OK OK 04/12/ Wien AT 4638,40 km² 30/09/ /11/2009 OK OK OK 04/12/ Warszawa PL 5229,75 km² 11/05/ /06/2009 OK OK OK 04/12/ Lisboa PT 1452,68 km² 13/04/ /04/2009 OK OK OK 04/12/ Ljubljana SL 2573,05 km² 06/04/ /06/2009 OK OK OK 04/12/ Bratislava SK 2055,36 km² 07/05/ /05/2009 OK OK OK 04/12/ Bucarest RO 1086,51 km² 11/05/ /05/2009 OK OK OK 04/12/ Antwerpen BE 952,00 km² 25/02/ /03/2009 OK OK OK 04/12/ Brno CZ 3322,10 km² 14/06/ /07/2009 OK OK OK 04/12/ Thessaloniki GR 1436,94 km² 25/05/ /06/2009 OK OK OK 04/12/ Barcelona ES 1809,58 km² 16/04/ /04/2009 OK OK OK 04/12/ Marseille FR 609,28 km² 13/05/ /05/2009 OK OK OK 04/12/ Milano IT 1361,43 km² 07/04/ /04/2009 OK OK OK 04/12/ Liepaja LV 3666,39 km² 17/04/ /04/2009 OK OK OK 04/12/ Kaunas LT 1635,10 km² 27/02/ /03/2009 OK OK OK 04/12/ Miskolc HU 1015,11 km² 25/02/ /03/2009 OK OK OK 04/12/ Rotterdam NL 713,66 km² 24/02/ /03/2009 OK OK OK 04/12/ Graz AT 1243,17 km² 04/03/ /03/2009 OK OK OK 04/12/ Lodz PL 2875,44 km² 16/06/ /08/2009 OK OK OK 04/12/ Kosice SK 1783,67 km² 15/04/ /04/2009 OK OK OK 04/12/ Page 70/96

71 31 Tempere FI 2392,63 km² 28/05/ /07/2009 OK OK OK 04/12/ Liege BE 1067,06 km² 20/04/ /05/2009 OK OK OK 04/12/ Munchen DE 5238,55 km² 28/08/ /10/2009 OK OK OK 04/12/ Koln DE 1634,70 km² 07/04/ /04/2009 OK OK OK 04/12/ Patrai GR 510,58 km² 25/02/ /03/2009 OK OK OK 04/12/ Valencia ES 1454,65 km² 03/06/ /07/2009 OK OK OK 04/12/ Sevilla ES 3093,06 km² 04/05/ /05/2009 OK OK OK 04/12/ Lyon FR 3339,29 km² 20/04/ /07/2009 OK OK OK 04/12/ Lille FR 620,09 km² 16/06/ /07/2009 OK OK OK 04/12/ Napoli IT 571,40 km² 02/06/ /06/2009 OK OK OK 04/12/ Panevezys LT 2243,19 km² 28/08/ /10/2009 OK OK OK 04/12/ Debrecen HU 1687,67 km² 31/07/ /08/2009 OK OK OK 04/12/ Utrecht NL 394,91 km² 11/06/ /06/2009 OK OK OK 04/12/ S'GRAVENHAGE NL 421,28 km² 25/05/ /06/2009 OK OK OK 04/12/ Linz AT 1762,08 km² 11/09/ /10/2009 OK OK OK 04/12/ Braga PT 499,94 km² 16/06/ /07/2009 OK OK OK 04/12/ Malmö SE 1852,48 km² 25/08/ /10/2009 OK OK OK 04/12/ Gent BE 546,57 km² 03/05/ /06/2009 OK OK OK 04/12/ Dresden DE 2635,29 km² 02/11/ /11/2009 OK OK OK 04/12/ Trier DE 1217,41 km² 16/07/ /07/2009 OK OK OK 04/12/ Iraklion GR 605,95 km² 20/04/ /04/2009 OK OK OK 04/12/ Zaragoza ES 2304,94 km² 01/07/ /07/2009 OK OK OK 04/12/ Nice FR 334,65 km² 07/08/ /08/2009 OK OK OK 04/12/ Metz FR 1854,83 km² 02/07/ /07/2009 OK OK OK 04/12/ Palermo IT 1190,49 km² 17/07/ /08/2009 OK OK OK 04/12/ Firenze IT 1273,13 km² 01/07/ /07/2009 OK OK OK 04/12/ Pecs HU 578,28 km² 29/07/ /08/2009 OK OK OK 04/12/ Eindhoven NL 332,64 km² 22/05/ /05/2009 OK OK OK 04/12/ Poznan PL 3738,26 km² 28/08/ /10/2009 OK OK OK 04/12/ Coimbra PT 1264,74 km² 13/10/ /11/2009 OK OK OK 04/12/ Cluj Napoca RO 599,60 km² 05/08/ /08/2009 OK OK OK 04/12/ Timisoara RO 241,25 km² 06/05/ /05/2009 OK OK OK 04/12/ Craiova RO 347,57 km² 17/07/ /07/2009 OK OK OK 04/12/ Braila RO 442,25 km² 06/08/ /08/2009 OK OK OK 04/12/ Plovdiv BG 1235,84 km² 03/07/ /07/2009 OK OK OK 04/12/ Charleroi BE 628,14 km² 24/06/ /07/2009 OK OK OK 04/12/ Brugge BE 412,00 km² 08/06/ /06/2009 OK OK OK 04/12/ Namur BE 404,02 km² 06/05/ /05/2009 OK OK OK 04/12/ Usti nad Labem CZ 880,00 km² 19/05/ /06/2009 OK OK OK 04/12/ Olomouc CZ 1629,92 km² 28/07/ /08/2009 OK OK OK 04/12/ Liberec CZ 1339,86 km² 12/10/ /11/2009 OK OK OK 04/12/ Page 71/96

72 72 Hannover DE 2993,81 km² 28/08/ /10/2009 OK OK OK 04/12/ Wuppertal DE 167,81 km² 06/05/ /05/2009 OK OK OK 04/12/ Karlsruhe DE 1268,72 km² 07/07/ /07/2009 OK OK OK 04/12/ Mönchengladbach DE 171,21 km² 22/04/ /04/2009 OK OK OK 04/12/ Larisa GR 1567,87 km² 110/07/ /07/2009 OK OK OK 04/12/ Volos GR 308,82 km² 17/06/ /06/2009 OK OK OK 04/12/ loannina GR 1339,41 km² 07/07/ /07/2009 OK OK OK 04/12/ Las Palmas ES 875,33 km² 02/07/ /07/2009 OK OK OK 04/12/ Bilbao ES 991,32 km² 26/05/ /06/2009 OK OK OK 04/12/ Palma di Mallorca ES 2174,65 km² 05/08/ /09/2009 OK OK OK 04/12/ Valladolid ES 3055,61 km² 27/08/ /09/2009 OK OK OK 04/12/ Cordoba ES 1265,45 km² 30/06/ /07/2009 OK OK OK 04/12/ Vigo ES 1435,67 km² 26/08/ /10/2009 OK OK OK 04/12/ Gijon ES 524,70 km² 14/08/ /08/2009 OK OK OK 04/12/ Montpellier FR 599,07 km² 06/08/ /08/2009 OK OK OK 04/12/ Toulon FR 340,72 km² 06/08/ /08/2009 OK OK OK 04/12/ Le Havre FR 641,72 km² 08/10/ /10/2009 OK OK OK 04/12/ Galway IE 51,95 km² 02/11/ /11/2009 OK OK OK 04/12/ Trieste IT 215,22 km² 11/08/ /08/2009 OK OK OK 04/12/ Brescia IT 548,75 km² 12/08/ /08/2009 OK OK OK 04/12/ Szeged HU 760,28 km² 22/10/ /11/2009 OK OK OK 04/12/ Tilburg NL 394,39 km² 02/11/ /11/2009 OK OK OK 04/12/ Groningen NL 949,09 km² 06/11/ /11/2009 OK OK OK 04/12/ Nijmegen NL 322,00 km² 02/11/ /11/2009 OK OK OK 04/12/ Breda NL 503,76 km² 06/11/ /11/2009 OK OK OK 04/12/ Banska Bystrica SK 815,47 km² 02/11/ /11/2009 OK OK OK 04/12/ Ostrava CZ 3904,63 km² 16/06/ /10/2009 OK OK OK 20/01/ Torino IT 1893,59 km² 19/11/ /12/2009 OK OK OK 20/01/ Krakow PL 3024,73 km² 14/10/ /11/2009 OK OK OK 20/01/ Bremen DE 5920,41 km² 25/08/ /11/2009 OK OK OK 20/01/ Bielefeld DE 2943,17 km² 02/11/ /12/2009 OK OK OK 20/01/ Reggio di Calabria IT 492,83 km² 01/12/ /12/2009 OK OK OK 20/01/ Gyor HU 1451,53 km² 15/12/ /01/2010 OK OK OK 20/01/ Apeldoorn NL 630,94 km² 17/11/ /11/2009 OK OK OK 20/01/ Lublin PL 2902,02 km² 30/11/ /12/2009 OK OK OK 20/01/ Torun PL 1360,06 km² 01/12/ /12/2009 OK OK OK 20/01/ Olzstyn PL 2949,00 km² 08/12/ /12/2009 OK OK OK 20/01/ Presov SK 944,63 km² 27/11/ /12/2009 OK OK OK 20/01/ Jonkoping SE 3491,95 km² 02/11/ /12/2009 OK OK OK 20/01/ Darmstadt DE 790,62 km² 15/12/ /12/2009 OK OK OK 20/01/ Frankfurt (Oder) DE 151,48 km² 28/12/ /01/2010 OK OK OK 20/01/ Page 72/96

73 113 Kavala GR 355,57 km² 28/12/ /01/2010 OK OK OK 20/01/ Kalamata GR 447,36 km² 28/12/ /01/2010 OK OK OK 20/01/ Lens - Lievin FR 243,99 km² 08/01/ /01/2010 OK OK OK 20/01/ Pescara IT 683,45 km² 15/12/ /01/2010 OK OK OK 20/01/ Ancona IT 409,83 km² 15/12/ /01/2009 OK OK OK 20/01/ Nitra SK 878,07 km² 12/01/ /01/2010 OK OK OK 26/02/ Alicante ES 679,03 km² 12/01/ /01/2010 OK OK OK 26/02/ Clermont Ferrand FR 1833,60 km² 08/01/ /02/2010 OK OK OK 26/02/ Nancy FR 1856,04 km² 05/01/ /01/2010 OK OK OK 26/02/ Catania IT 591,58 km² 25/01/ /02/2010 OK OK OK 26/02/ Taranto IT 1427,07 km² 08/12/ /01/2010 OK OK OK 26/02/ Modena IT 647,57 km² 25/01/ /02/2010 OK OK OK 26/02/ Cagliari IT 1695,82 km² 08/12/ /01/2010 OK OK OK 26/02/ Gdansk PL 3351,19 km² 08/12/ /01/2010 OK OK OK 26/02/ Kielce PL 2373,78 km² 26/11/ /12/2009 OK OK OK 26/02/ Radom PL 1644,72 km² 15/12/ /01/2010 OK OK OK 26/02/ Setubal PT 177,02 km² 07/01/ /01/2010 OK OK OK 26/02/ Aveiro PT 275,07 km² 11/01/ /01/2010 OK OK OK 26/02/ Sheffield UK 1878,19 km² 27/01/ /02/2010 OK OK OK 26/02/ Leicester UK 1407,80 km² 12/01/ /01/2010 OK OK OK 26/02/ Oradea RO 205,95 km² 19/01/ /01/2010 OK OK OK 26/02/ Bacau RO 225,45 km² 12/01/ /01/2010 OK OK OK 26/02/ Arad RO 527,64 km² 20/01/ /01/2010 OK OK OK 26/02/ Sibiu RO 597,74 km² 19/01/ /01/2010 OK OK OK 26/02/ Varna BG 887,78 km² 26/01/ /02/2010 OK OK OK 26/02/ Burgas BG 1402,52 km² 12/01/ /02/2010 OK OK OK 26/02/ Hradec Kralove CZ 879,82 km² 25/01/ /02/2010 OK OK OK 26/02/ Pardubice CZ 901,41 km² 21/01/ /02/2010 OK OK OK 26/02/ Zlin CZ 1040,37 km² 01/02/ /02/2010 OK OK OK 26/02/ Jihlava CZ 1191,10 km² 22/01/ /02/2010 OK OK OK 26/02/ Dusseldorf DE 1207,09 km² 01/02/ /02/2010 OK OK OK 26/02/ Santander ES 597,97 km² 19/01/ /02/2010 OK OK OK 26/02/ Santa Cruz de Tenerife ES 608,49 km² 26/01/ /02/2010 OK OK OK 26/02/ Foggia IT 1059,32 km² 25/01/ /02/2010 OK OK OK 26/02/ Trento IT 787,41 km² 20/01/ /02/2010 OK OK OK 26/02/ Catanzaro IT 771,39 km² 21/01/ /02/2010 OK OK OK 26/02/ Caserta IT 678,96 km² 21/01/ /01/2010 OK OK OK 26/02/ Cremona IT 669,47 km² 26/01/ /02/2010 OK OK OK 26/02/ Faro PT 488,32 km² 25/01/ /01/2010 OK OK OK 26/02/ Piatra Neamt RO 150,31 km² 19/02/ /02/2010 OK OK OK 26/02/ Calarasi RO 249,38 km² 16/02/ /02/2010 OK OK OK 26/02/ Page 73/96

74 154 Giurgiu RO 113,32 km² 16/02/ /02/2010 OK OK OK 26/02/ Alba Iulia RO 264,02 km² 17/02/ /02/2010 OK OK OK 26/02/ Ceske Budějovice CZ 1638,78 km² 29/01/ /03/2010 OK OK OK 22/03/ Nantes FR 2322,91 km² 26/01/ /03/2010 OK OK OK 22/03/ Strasbourg FR 1382,84 km² 02/03/ /03/2010 OK OK OK 22/03/ Saint-Etienne FR 575,48 km² 25/01/ /03/2010 OK OK OK 22/03/ Orleans FR 2085,79 km² 18/03/ /03/2010 OK OK OK 22/03/ Venezia IT 1215,81 km² 11/01/ /03/2010 OK OK OK 22/03/ Belfast UK 968,22 km² 24/02/ /03/2010 OK OK OK 22/03/ Saarbrucken DE 1552,03 km² 02/03/ /03/2010 OK OK OK 22/03/ Málaga ES 952,67 km² 28/01/ /03/2010 OK OK OK 22/03/ Badajoz ES 1487,68 km² 26/02/ /03/2010 OK OK OK 22/03/ Amiens FR 1783,98 km² 01/03/ /03/2010 OK OK OK 22/03/ Reims FR 1810,41 km² 02/03/ /03/2010 OK OK OK 22/03/ Salerno IT 955,86 km² 25/01/ /03/2010 OK OK OK 22/03/ Stoke-on-trent UK 890,10 km² 02/03/ /03/2010 OK OK OK 22/03/ Targu Mures RO 145,67 km² 26/02/ /03/2010 OK OK OK 22/03/ Ruse BG 898,95 km² 26/02/ /03/2010 OK OK OK 22/03/ Murcia ES 1334,60 km² 31/03/ /04/2010 OK OK OK 29/04/ Grenoble FR 1618,78 km² 13/01/ /04/2010 OK OK OK 29/04/ Tours FR 1825,20 km² 12/03/ /03/2010 OK OK OK 29/04/ Bari IT 901,27 km² 10/03/ /03/2010 OK OK OK 29/04/ Verona IT 1220,18 km² 02/02/ /03/2010 OK OK OK 29/04/ Padova IT 988,28 km² 20/01/ /04/2010 OK OK OK 29/04/ Bydgoszcz PL 3416,42 km² 19/03/ /04/2010 OK OK OK 29/04/ Katowice PL 2658,55 km² 13/01/ /03/2010 OK OK OK 29/04/ Santiago de Compostela ES 1366,16 km² 07/04/ /04/2010 OK OK OK 29/04/ Logrono ES 1448,93 km² 06/04/ /04/2010 OK OK OK 29/04/ Perugia IT 814,53 km² 14/04/ /04/2010 OK OK OK 29/04/ Székesfehérvár HU 1154,24 km² 06/04/ /04/2010 OK OK OK 29/04/ Enschede NL 357,92 km² 09/04/ /04/2009 OK OK OK 29/04/ Arnhem NL 492,28 km² 02/04/ /04/2010 OK OK OK 29/04/ Leeuwarden NL 455,99 km² 13/04/ /04/2010 OK OK OK 29/04/ Plock PL 1899,85 km² 31/03/ /04/2010 OK OK OK 29/04/ Koszalin PL 1762,20 km² 01/04/ /04/2010 OK OK OK 29/04/ Trnava SK 748,64 km² 06/04/ /04/2010 OK OK OK 29/04/ Trencin SK 681,01 km² 20/04/ /04/2010 OK OK OK 29/04/ Cambridge UK 952,37 km² 15/03/ /03/2010 OK OK OK 29/04/ Wolverhampton UK 484,08 km² 02/04/ /04/2010 OK OK OK 29/04/ Nottingham UK 910,66 km² 09/03/ /04/2010 OK OK OK 29/04/ Vidin BG 524,97 km² 18/03/ /03/2010 OK OK OK 29/04/ Page 74/96

75 195 Rennes FR 2575,62 km² 31/03/ /04/2010 OK OK OK 18/05/ Aix en Provence FR 1305,51 km² 27/04/ /05/2010 OK OK OK 18/05/ Bologna IT 2065,98 km² 11/01/ /04/2010 OK OK OK 18/05/ Czestochowa PL 2586,64 km² 19/04/ /05/2010 OK OK OK 18/05/ Vitoria-Gasteiz ES 2334,43 km² 31/03/ /04/2010 OK OK OK 18/05/ Heerlen NL 215,68 km² 12/04/ /04/2010 OK OK OK 18/05/ Gorzów Wielkopolski PL 1317,55 km² 12/04/ /05/2010 OK OK OK 18/05/ Zielona Góra PL 1641,18 km² 28/04/ /05/2010 OK OK OK 18/05/ Konin PL 767,03 km² 20/04/ /05/2010 OK OK OK 18/05/ Zilina SK 824,54 km² 31/03/ /04/2010 OK OK OK 18/05/ Leipzig DE 2818,46 km² 29/05/ /06/2010 OK OK OK 23/06/ Oulu FI 3773,58 km² 28/04/ /05/2010 OK OK OK 23/06/ Linkoping SE 4252,50 km² 06/05/ /06/2010 OK OK OK 23/06/ Magdeburg DE 4350,33 km² 03/05/ /06/2010 OK OK OK 23/06/ Oviedo ES 2354,95 km² 06/05/ /05/2010 OK OK OK 23/06/ Toledo ES 3638,13 km² 03/05/ /05/2010 OK OK OK 23/06/ Dijon FR 2300,63 km² 28/04/ /05/2010 OK OK OK 23/06/ Poitiers FR 1773,87 km² 27/04/ /05/2010 OK OK OK 23/06/ Potenza IT 1513,30 km² 03/05/ /05/2010 OK OK OK 23/06/ l'aquila IT 1599,70 km² 07/05/ /05/2010 OK OK OK 23/06/ Kalisz PL 3123,16 km² 29/04/ /06/2010 OK OK OK 23/06/ Pleven BG 1807,06 km² 26/04/ /05/2010 OK OK OK 23/06/ Athens GR 3031,26 km² 11/06/ /07/2010 OK OK OK 23/07/ Tartu EE 3017,94 km² 22/06/ /07/2010 OK OK OK 23/07/ Wroclaw PL 4608,38 km² 12/05/ /06/2010 OK OK OK 23/07/ Frankfurt am Main DE 4309,05 km² 03/05/ /06/2010 OK OK OK 23/07/ Bialystok PL 5140,38 km² 15/06/ /07/2010 OK OK OK 23/07/ Orebro SE 3707,05 km² 14/06/ /06/2010 OK OK OK 23/07/ Pamplona-Iruna ES 4405,30 km² 11/06/ /07/2010 OK OK OK 23/07/ London UK 8991,68 km² 22/06/ /07/2010 OK OK OK 30/08/ Toulouse FR 4064,45 km² 17/06/ /07/2010 OK OK OK 30/08/ Umea SE 9792,33 km² 27/06/ /07/2010 OK OK OK 30/08/ Odense DK 3487,07 km² 06/07/ /08/2010 OK OK OK 30/08/ Schwerin DE 4915,06 km² 19/07/ / OK OK OK 30/08/ Koblenz DE 934,08 km² 22/04/ /04/2010 OK OK OK 30/08/ Madrid ES 8054,23 km² 06/07/ /10/2010 OK OK OK 13/10/ Plzen CZ 3124,93 km² 23/07/ /10/2010 OK OK OK 13/10/ Bordeaux FR 3909,90 km² 23/06/ /10/2010 OK OK OK 13/10/ Szczecin PL 6078,12 km² 06/08/ /09/2010 OK OK OK 13/10/ Besançon FR 1682,77 km² 09/07/ /09/2010 OK OK OK 13/10/ Campobasso IT 1320,38 km² 16/07/ /09/2010 OK OK OK 13/10/ Page 75/96

76 236 Nyíregyháza HU 1448,92 km² 21/07/ /08/2010 OK OK OK 13/10/ Kecskemét HU 1493,78 km² 21/07/ /08/2010 OK OK OK 13/10/ Jelenia Góra PL 592,93 km² 29/04/ /08/2010 OK OK OK 13/10/ Berlin DE 17504,83 km² 23/09/ /10/2010 OK OK OK 21/10/ Sofia BG 3435,78 km² 14/06/ /11/2010 OK OK OK 26/11/ Aarhus DK 4554,46 km² 06/07/ /11/2010 OK OK OK 26/11/ Gozo MT 68,03 km² 28/04/ /04/2010 OK OK OK 26/11/ Liverpool UK 650,57 km² 10/08/ /11/2010 OK OK OK 26/11/ Genova IT 933,65 km² 29/04/ /11/2010 OK OK OK 26/11/ Edinburgh UK 1739,52 km² 16/07/ /11/2010 OK OK OK 26/11/ Waterford IE 43,19 km² 17/03/ /03/2010 OK OK OK 26/11/ Weimar DE 890,88 km² 26/10/ /11/2010 OK OK OK 26/11/ Caen FR 1630,38 km² 19/10/ /11/2010 OK OK OK 26/11/ Ajaccio FR 1024,68 km² 17/06/ /10/2010 OK OK OK 26/11/ Derry UK 390,46 km² 20/10/ /11/2010 OK OK OK 26/11/ Stara Zagora BG 88,14 km² 15/10/ /10/2010 OK OK OK 26/11/ Helsinki FI 3095,87 km² 06/07/ /12/2010 OK OK OK 10/01/ Turku FI 1746,07 km² 07/12/ /12/2010 OK OK OK 10/01/ Coventry UK 820,94 km² 17/12/ /12/2010 OK OK OK 10/01/ Karlovy Vary CZ 1630,00 km² 20/10/ /12/2010 OK OK OK 10/01/ Sassari IT 1230,36 km² 22/06/ /12/2010 OK OK OK 10/01/ Opole PL 1708,14 km² 17/12/ /12/2011 OK OK OK 10/01/ Wrexham UK 955,58 km² 02/11/ /12/2010 OK OK OK 10/01/ Worcester UK 1289,73 km² 02/12/ /12/2010 OK OK OK 10/01/ Cork IE 2112,87 km² 07/12/ /02/2011 OK OK OK 23/02/ Oporto PT 567,08 km² 04/01/ /02/2011 OK OK OK 23/02/ Göteborg SE 4220,49 km² 02/12/ /01/2011 OK OK OK 23/02/ Birmingham UK 1596,74 km² 08/02/ /02/2011 OK OK OK 23/02/ Uppsala SE 6849,51 km² 22/12/ /02/2011 OK OK OK 23/02/ Paris FR 12106,34 km² 28/01/ /03/2011 OK OK OK 31/03/ Riga LV 5413,94 km² 28/02/ /03/2011 OK OK OK 31/03/ Kiel DE 3394,09 km² 09/02/ /03/2011 OK OK OK 31/03/ Nowy Sacz PL 454,65 km² 14/02/ /03/2011 OK OK OK 31/03/ Ponto Delgada PT 536,91 km² 15/02/ /03/2011 OK OK OK 31/03/ Essen DE 4462,57 km² 08/02/ /05/2011 OK OK OK 13/05/ Bonn DE 1300,33 km² 09/05/ /05/2011 OK OK OK 13/05/ Funchal PT 260,00 km² 10/05/ /05/2011 OK OK OK 13/05/ Portsmouth UK 202,69 km² 05/05/ /05/2011 OK OK OK 13/05/ Erfurt DE 2879,46 km² 15/02/ /04/2011 OK OK OK 13/05/ Mainz DE 708,18 km² 29/04/ /05/2011 OK OK OK 13/05/ Limoges FR 1855,93 km² 24/02/ /04/2011 OK OK OK 13/05/ Page 76/96

77 277 Rzeszów PL 1287,50 km² 06/04/ /04/2011 OK OK OK 13/05/ Dublin IE 7013,88 km² 16/05/ /06/2011 OK OK OK 01/07/ Stockholm SE 7005,77 km² 11/05/ /06/2011 OK OK OK 01/07/ Maribor SL 2185,66 km² 09/05/ /05/2011 OK OK OK 01/07/ Salzburg AT 1757,81 km² 20/06/ /06/2011 OK OK OK 01/07/ Cardiff UK 1185,35 km² 21/04/ /05/2011 OK OK OK 01/07/ Wiesbaden DE 1027,10 km² 28/02/ /06/2011 OK OK OK 01/07/ Freiburg im Breisgau DE 2226,95 km² 16/05/ /06/2011 OK OK OK 01/07/ Limerick IE 3541,09 km² 15/06/ /07/2011 OK OK OK 29/07/ Manchester UK 1287,70 km² 15/06/ /07/2011 OK OK OK 29/07/ Aalborg DK 6169,39 km² 07/07/ /07/2011 OK OK OK 29/07/ Halle an der Saale DE 1583,08 km² 14/06/ /07/2011 OK OK OK 29/07/ Göttingen DE 2405,87 km² 13/07/ /07/2011 OK OK OK 29/07/ Augsburg DE 2001,06 km² 20/06/ /07/2011 OK OK OK 29/07/ Exeter UK 2466,69 km² 12/07/ /07/2011 OK OK OK 29/07/ Kingston-upon-Hull UK 2500,06 km² 15/06/ /07/2011 OK OK OK 29/07/ Hamburg DE 7232,45 km² 03/08/ /08/2011 OK OK OK 02/09/ Nürnberg DE 2725,27 km² 12/08/ /08/2011 OK OK OK 02/09/ Rouen FR 1600,20 km² 19/08/ /08/2011 OK OK OK 02/09/ Innsbruck AT 2104,27 km² 12/08/ /08/2011 OK OK OK 02/09/ Newcastle upon Tyne UK 3401,18 km² 15/06/ /09/2011 OK OK OK 02/09/ Suwalki PL 625,47 km² 22/08/ /08/2011 OK OK OK 02/09/ Lincoln UK 731,55 km² 25/07/ /09/2011 OK OK OK 02/09/ Leeds UK 5131,92 km² 07/09/ /09/2011 OK OK OK 30/09/ Glasgow UK 3390,53 km² 01/09/ /09/2011 OK OK OK 30/09/ Stuttgart DE 3679,26 km² 12/09/ /09/2011 OK OK OK 30/09/ Bristol UK 1343,86 km² 22/08/ /09/2011 OK OK OK 30/09/ Aberdeen UK 6526,44 km² 23/08/ /09/2011 OK OK OK 30/09/ Regensburg DE 2392,36 km² 13/09/ /09/2011 OK OK OK 30/09/ Page 77/96

78 Appendix 2 : Results of confusion matrices by LUZ Luz Nb of points Urban points Rural points Urban Accuracy Rural Accuracy Overall Accuracy Aalborg ,50% 91,12% 85,74% Aarhus ,02% 94,91% 90,66% Aberdeen ,59% 93,23% 88,29% Aix-en-Provence ,38% 92,86% 89,52% Ajaccio ,83% 90,12% 87,04% Alba_Iulia ,40% 98,85% 95,40% Alicante_Alacant ,89% 91,45% 88,03% Amiens ,43% 93,73% 87,71% Amsterdam ,40% 97,48% 96,40% Ancona ,91% 98,48% 90,91% Antwerpen ,17% 97,38% 94,90% Apeldoorn ,42% 95,06% 85,19% Arad ,43% 96,43% 92,86% Arnhem ,53% 95,79% 90,53% Athina ,41% 93,67% 89,24% Augsburg ,04% 96,49% 92,98% Aveiro ,12% 94,12% 88,24% Bacau ,00% 92,50% 87,50% Badajoz ,36% 92,53% 90,04% Banska_Bystrica ,27% 90,30% 85,45% Barcelona ,41% 93,18% 89,55% Bari ,51% 93,43% 89,05% Belfast ,16% 96,13% 89,50% Berlin ,13% 93,17% 85,17% Besançon ,33% 93,09% 87,57% Bialystok ,08% 95,70% 89,97% Bielefeld ,50% 93,77% 88,43% Bilbao ,62% 96,35% 94,16% Birmingham ,55% 95,79% 86,78% Bologna ,74% 95,24% 88,28% Bonn ,76% 94,29% 87,76% Bordeaux ,99% 91,29% 86,56% Braga ,52% 95,52% 94,03% Braila ,04% 96,30% 87,04% Bratislava ,92% 95,64% 88,83% Breda ,30% 96,81% 88,30% Bremen ,72% 94,20% 88,80% Brescia ,72% 95,61% 86,84% Bristol ,16% 94,29% 87,35% Brno ,26% 92,33% 86,46% Brugge ,81% 97,62% 97,62% Bruxelles ,44% 93,10% 88,45% Bucuresti ,83% 98,59% 96,83% Budapest ,52% 93,65% 85,23% Burgas ,54% 89,06% 87,50% Bydgoszcz ,46% 94,73% 88,06% Page 78/96

79 Caen ,99% 95,41% 88,07% Cagliari ,64% 95,09% 92,41% Calarasi ,86% 91,67% 89,29% Cambridge ,46% 97,39% 92,81% Campobasso ,10% 91,38% 87,93% Cardiff ,20% 91,20% 87,60% Caserta ,28% 96,64% 91,60% Catania ,17% 96,52% 91,30% Catanzaro ,39% 92,52% 88,79% Ceske_Budejovice ,70% 92,42% 88,79% Charleroi ,56% 97,22% 95,56% Clermont-Ferrand ,20% 92,40% 89,60% Cluj-Napoca ,99% 93,50% 85,37% Coimbra ,14% 90,51% 86,08% Cordoba ,07% 92,14% 86,79% Cork ,00% 94,40% 85,60% Coventry ,43% 92,35% 86,89% Craiova ,33% 89,33% 85,33% Cremona ,42% 97,37% 93,42% Czestochowa ,65% 92,25% 87,39% Darmstadt ,51% 96,34% 93,29% Debrecen ,05% 88,15% 85,31% Derry ,59% 100,00% 93,59% Dijon ,98% 94,27% 87,40% Dresden ,13% 93,29% 85,01% Dublin ,24% 93,27% 85,85% Dusseldorf ,22% 98,60% 90,22% Edinburgh ,52% 94,12% 87,96% Eindhoven ,25% 96,55% 93,10% Enschede ,18% 98,53% 91,18% Erfurt ,08% 94,66% 89,57% Essen ,15% 94,21% 85,66% Exeter ,41% 88,19% 85,61% Faro ,46% 90,24% 86,59% Firenze ,30% 92,98% 85,38% Foggia ,02% 97,09% 93,02% Frankfurt_(Oder) ,00% 94,29% 90,00% Frankfurt_am_Main ,37% 96,39% 90,60% Freiburg im Breisgau ,70% 90,79% 85,77% Funchal ,15% 87,38% 85,44% Galway ,04% 92,54% 91,04% Gdansk ,72% 92,23% 87,47% Genova ,76% 89,92% 87,39% Gent ,66% 98,06% 94,66% Gijon ,54% 85,71% 74,60% Giurgiu ,06% 99,03% 98,06% Glasgow ,47% 92,67% 86,65% Gorzow_Wielkopolski ,71% 92,61% 85,22% Page 79/96

80 Gozo ,65% 95,65% 94,20% Graz ,42% 95,29% 92,01% Grenoble ,00% 96,21% 89,10% Groningen ,67% 93,84% 87,67% Gyor ,60% 86,53% 86,01% Göteborg ,09% 92,63% 87,25% Göttingen ,50% 96,08% 91,99% Halle an der Saale ,23% 91,58% 85,60% Hamburg ,37% 95,68% 88,87% Hannover ,47% 93,92% 85,14% Heerlen ,57% 92,54% 86,57% Helsinki ,22% 90,73% 85,78% Hradec_Kralove ,48% 97,14% 89,52% Innsbruck ,46% 94,50% 90,31% Ioannina ,71% 88,50% 85,40% Iraklion ,30% 98,15% 96,30% Jelenia_Gora ,41% 92,75% 86,96% Jihlava ,72% 93,46% 88,79% Jonkoping ,38% 88,61% 85,91% Kalamata ,78% 91,78% 86,30% Kalisz ,57% 96,57% 91,81% Karlovy Vary ,00% 90,36% 88,21% Karlsruhe ,56% 96,37% 84,97% Katowice ,27% 91,91% 86,07% Kaunas ,02% 90,59% 85,02% Kavala ,32% 90,91% 88,64% Kecskemet ,56% 94,46% 89,97% Kielce ,23% 92,55% 87,99% Kiel ,73% 95,49% 89,97% Kingston-upon-Hull ,89% 93,77% 86,56% Kobenhavn ,88% 98,13% 95,66% Koblenz ,05% 96,69% 92,05% Koln ,15% 98,25% 94,15% Konin ,79% 92,79% 90,09% Kosice ,36% 93,98% 88,35% Koszalin ,47% 93,72% 90,79% Krakow ,29% 92,66% 85,88% Larisa ,71% 92,37% 86,86% Las_Palmas ,61% 96,10% 89,61% Leeds ,73% 92,16% 85,13% Leeuwarden ,61% 96,10% 88,31% Lefkosia ,59% 92,04% 88,05% Leicester ,34% 97,02% 92,34% Leipzig ,31% 90,73% 87,50% Lens_Lievin ,95% 92,77% 86,75% Le_Havre ,25% 92,62% 85,91% Liberec ,84% 92,69% 85,67% Liepaja ,42% 92,96% 88,17% Page 80/96

81 Lille ,92% 95,15% 85,44% Limerick ,78% 93,39% 89,18% Limoges ,90% 93,15% 86,30% Lincoln ,43% 89,26% 85,95% Linköping ,38% 95,17% 91,06% Linz ,96% 96,06% 88,53% Lisboa ,47% 91,34% 85,20% Liverpool ,66% 92,28% 84,93% Liège ,33% 98,78% 96,02% Ljubljana ,38% 94,02% 87,84% Lodz ,13% 94,31% 85,40% Logrono ,58% 97,95% 94,52% London ,53% 92,91% 86,82% Lublin ,94% 93,35% 87,53% Luxembourg ,80% 98,55% 96,22% Lyon ,87% 94,59% 91,71% l_aquila ,41% 95,24% 94,44% Madrid ,99% 91,75% 85,42% Magdeburg ,10% 94,14% 87,57% Mainz ,71% 92,31% 85,31% Malaga ,25% 95,07% 91,55% Malmö ,00% 95,43% 85,71% Manchester ,80% 93,80% 86,80% Maribor ,25% 95,97% 88,98% Marseille ,24% 92,68% 87,20% Metz ,86% 97,59% 95,52% Milano ,42% 96,25% 88,21% Miskolc ,40% 93,81% 89,78% Modena ,57% 96,74% 92,39% Monchengladbach ,69% 99,14% 95,69% Montpellier ,26% 90,08% 85,12% Murcia ,11% 91,98% 85,49% München ,11% 95,26% 86,23% Namur ,06% 99,03% 98,06% Nancy ,27% 90,56% 86,27% Nantes ,77% 94,46% 88,92% Napoli ,44% 95,95% 86,13% Newcastle upon Tyne ,98% 93,51% 85,88% Nice ,86% 90,00% 88,57% Nijmegen ,83% 98,31% 89,83% Nitra ,07% 92,05% 88,74% Nottingham ,49% 95,74% 90,64% Nowy Sacz ,67% 89,33% 86,67% Nyiregyhaza ,91% 95,45% 90,91% Nürnberg ,95% 95,98% 90,14% Odense ,99% 96,60% 91,50% Olomouc ,05% 92,54% 87,56% Olsztyn ,45% 94,59% 90,31% Page 81/96

82 Opole ,43% 94,65% 90,79% Oporto ,89% 93,43% 86,87% Oradea ,58% 95,83% 89,58% Orebro ,15% 92,36% 87,44% Orleans ,97% 96,75% 90,97% Ostrava ,60% 92,65% 86,68% Oulu ,20% 91,12% 86,48% Oviedo ,02% 94,02% 89,64% Padova ,82% 98,09% 92,82% Palermo ,63% 93,81% 86,60% Palma_di_Mallorca ,31% 93,02% 87,60% Pamplona_Iruna ,52% 93,74% 91,65% Panevezys ,20% 94,96% 89,36% Pardubice ,33% 95,24% 91,43% Paris ,71% 94,30% 86,97% Patra ,17% 96,62% 93,72% Perugia ,82% 92,86% 87,76% Pescara ,00% 93,75% 87,50% Piatra_Neamt ,44% 93,33% 92,22% Pleven ,83% 96,76% 95,37% Plock ,02% 95,82% 91,63% Plovdiv ,63% 91,75% 87,11% Plzen ,85% 95,43% 89,91% Poitiers ,96% 95,96% 93,94% Ponto Delgada ,00% 98,02% 98,02% Portsmouth ,52% 92,13% 86,52% Potenza ,56% 95,12% 94,31% Poznan ,16% 91,97% 86,05% Praha ,29% 92,81% 86,11% Presov ,82% 94,55% 90,00% Pécs ,36% 91,36% 86,42% Radom ,27% 92,86% 90,48% Regensburg ,10% 93,59% 89,68% Reggio_di_Calabria ,64% 95,45% 88,64% Reims ,00% 95,75% 92,00% Rennes ,05% 96,33% 90,83% Riga ,49% 90,67% 84,99% Roma ,57% 91,91% 85,28% Rotterdam ,93% 95,63% 85,55% Rouen ,67% 92,59% 85,19% Ruse ,11% 92,59% 88,89% Rzeszów ,89% 93,83% 87,65% s'gravenhage ,79% 91,60% 87,79% Saarbrucken ,89% 92,26% 87,54% Saint-Etienne ,55% 97,85% 93,55% Salerno ,47% 91,58% 86,32% Salzburg ,54% 94,03% 88,06% Santander ,67% 92,00% 85,33% Page 82/96

83 Santa_Cruz_de_Tenerife ,87% 96,15% 92,31% Santiago_de_Compostela ,00% 88,00% 87,20% Sassari ,47% 91,73% 87,22% Schwerin ,16% 92,82% 86,34% Setubal ,83% 95,06% 92,59% Sevilla ,46% 94,02% 88,60% Sheffield ,36% 93,39% 87,30% Sibiu ,10% 91,95% 88,51% Sofia ,75% 95,11% 91,85% Stara Zagora ,77% 92,05% 88,64% Stockholm ,14% 92,17% 86,64% Stoke-on-trent ,19% 95,85% 90,67% Strasbourg ,97% 96,64% 94,30% Stuttgart ,21% 93,31% 88,42% Suwalki ,90% 92,74% 86,29% Szczecin ,11% 91,60% 86,27% Szeged ,26% 93,39% 88,43% Szekesfeharvar ,34% 98,62% 90,34% Tallinn ,82% 90,06% 85,00% Tampere ,47% 93,77% 87,54% Taranto ,76% 91,30% 88,04% Targu_Mures ,44% 97,22% 94,44% Tartu ,45% 91,74% 86,89% Thessaloniki ,67% 92,00% 85,33% Tilburg ,28% 98,61% 90,28% Timisoara ,00% 100,00% 100,00% Toledo ,74% 96,49% 93,48% Torino ,36% 92,12% 85,62% Torun ,56% 91,15% 85,90% Toulon ,76% 93,26% 85,39% Toulouse ,89% 95,37% 89,30% Tours ,16% 97,01% 91,04% Trencin ,68% 97,87% 94,68% Trento ,86% 95,45% 95,45% Trier ,30% 93,65% 85,71% Trieste ,18% 91,18% 85,29% Trnava ,79% 96,88% 94,79% Turku ,03% 93,04% 86,39% Umea ,48% 87,35% 85,40% Uppsala ,98% 91,13% 87,56% Usti_nad_Labem ,72% 89,47% 86,55% Utrecht ,62% 98,81% 97,62% Valencia ,36% 93,68% 85,87% Valladolid ,18% 95,06% 87,76% Valletta ,07% 96,43% 91,07% Varna ,14% 91,43% 89,71% Venezia ,69% 97,57% 93,69% Verona ,71% 95,31% 92,71% Page 83/96

84 Vidin ,33% 96,00% 93,33% Vigo ,81% 91,61% 86,71% Vilnius ,14% 92,51% 85,80% Vitoria_Gasteiz ,96% 94,54% 91,18% Volos ,37% 92,68% 85,37% Warszawa ,40% 88,89% 85,19% Waterford ,18% 94,12% 91,18% Weimar ,79% 91,38% 87,93% Wien ,99% 95,02% 87,66% Wiesbaden ,48% 93,94% 86,67% Wolverhampton ,44% 97,22% 93,52% Worcester ,92% 94,59% 93,92% Wrexham ,22% 91,62% 87,43% Wroclaw ,36% 94,96% 88,24% Wuppertal ,55% 100,00% 98,55% Zaragoza ,03% 95,93% 88,50% Zielona_Gora ,44% 92,22% 87,94% Zilina ,09% 93,95% 90,23% Zlin ,28% 91,60% 89,08% Page 84/96

85 Appendix 3 : Land/Use Cover statistics Area percentage by Country level 3 Countries Area (km²) Austria AT 11426,13 0,39 1,96 2,40 1,32 0,11 0,59 2,09 0,25 2,05 0,30 0,09 0,21 0,28 0,13 0,11 0,72 0,56 52,25 32,80 1,39 Belgium BE 5590,52 1,51 4,18 6,07 5,72 1,63 0,54 5,62 0,80 3,66 0,68 1,45 0,39 0,38 0,19 0,30 1,71 0,92 48,73 13,57 1,96 Bulgaria BG 10209,32 0,98 2,21 0,98 0,27 0,01 0,14 2,48 0,06 1,61 0,20 0,07 0,16 0,31 0,07 0,07 0,37 0,32 60,80 26,96 1,93 Cyprus CY 2710,23 1,33 2,06 0,47 0,28 0,18 0,41 1,51 0,07 1,19 0,00 0,00 0,29 0,46 0,05 0,24 0,14 0,15 90,71 0,31 0,15 Czech Republic CZ 28295,68 1,03 3,04 1,13 0,20 0,02 0,32 2,28 0,09 1,61 0,27 0,00 0,11 0,33 0,15 0,06 0,62 0,51 53,28 33,93 1,01 Germany DE ,46 1,21 4,28 1,63 0,34 0,02 0,40 3,67 0,26 2,31 0,33 0,09 0,13 0,45 0,11 0,10 0,80 1,14 53,38 27,61 1,72 Denmark DK 17188,46 0,32 2,87 2,12 1,11 0,09 1,72 2,52 0,16 1,99 0,13 0,13 0,16 0,20 0,12 0,07 0,64 1,07 69,10 13,63 1,86 Estonia EE 7336,77 0,18 0,83 1,04 0,36 0,01 0,95 1,05 0,00 0,99 0,10 0,09 0,14 0,22 0,13 0,04 0,30 0,13 40,57 51,69 1,16 Spain ES 47965,42 0,90 0,86 0,84 1,07 0,52 0,44 2,17 0,26 2,57 0,12 0,06 0,21 0,43 0,63 0,13 0,47 0,42 69,78 17,37 0,74 Finland FI 11008,48 0,13 0,39 0,80 1,82 1,71 1,25 1,69 0,19 1,78 0,13 0,10 0,21 0,41 0,14 0,04 0,91 0,53 24,74 56,30 6,75 France FR 61064,88 1,05 3,95 2,32 1,02 0,14 0,63 3,31 0,25 2,65 0,28 0,10 0,23 0,26 0,18 0,13 0,83 0,77 57,36 23,16 1,38 Greece GR 9563,02 1,83 2,37 1,55 1,04 0,16 0,47 2,80 0,15 2,75 0,07 0,17 0,31 0,43 0,16 0,13 0,39 0,30 72,48 11,70 0,74 Hungary HU 12028,59 2,34 4,96 1,29 0,29 0,01 0,41 2,91 0,14 1,61 0,27 0,01 0,22 0,27 0,26 0,17 0,55 0,45 60,97 21,49 1,39 Ireland IE 12746,69 0,13 1,92 1,83 0,81 0,04 1,75 1,92 0,11 1,56 0,08 0,06 0,12 0,47 0,17 0,11 0,52 1,29 75,70 9,05 2,36 Italia IT 35072,33 1,15 2,26 1,82 1,71 0,69 1,17 4,07 0,23 3,10 0,25 0,16 0,18 0,35 0,28 0,18 0,52 0,55 61,73 17,57 2,04 Lithuania LT 8095,35 0,84 1,99 0,82 0,08 0,01 0,90 1,54 0,07 1,16 0,10 0,00 0,07 0,12 0,21 0,10 0,85 0,17 50,86 38,04 2,06 Luxembourg LU 2596,59 0,31 2,63 1,56 0,10 0,00 0,14 2,43 0,25 2,44 0,22 0,02 0,15 0,23 0,14 0,07 0,23 0,49 51,60 36,51 0,48 Latvia LV 9044,75 0,25 0,80 0,84 0,51 0,08 0,78 1,26 0,00 1,14 0,12 0,10 0,12 0,20 0,03 0,09 0,41 0,35 38,57 50,86 3,47 Malta MT 315,58 3,41 4,37 2,72 2,17 1,53 0,70 6,40 0,00 6,71 0,00 0,59 1,09 1,33 0,14 0,34 0,45 0,77 66,64 0,05 0,58 Netherlands NL 7288,39 3,64 5,17 2,24 0,83 0,10 1,02 6,93 0,84 4,27 0,38 0,97 0,45 0,16 0,61 0,41 2,57 2,06 47,73 11,48 8,15 Poland PL 68644,09 1,54 3,06 0,59 0,04 0,00 0,79 1,95 0,04 1,49 0,23 0,02 0,09 0,22 0,13 0,07 0,40 0,51 57,22 29,92 1,70 Portugal PT 5469,78 3,33 4,29 2,61 1,62 0,33 0,73 3,97 0,34 3,72 0,16 0,18 0,27 0,46 0,46 0,43 0,94 0,61 49,65 22,48 3,42 Romania RO 5111,96 5,20 3,04 0,29 0,04 0,00 0,20 4,78 0,02 1,66 0,40 0,06 0,37 0,20 0,26 0,18 0,53 0,26 63,19 16,88 2,42 Suede SE 41306,67 0,04 0,10 0,38 1,24 1,57 0,82 0,99 0,11 1,29 0,09 0,04 0,08 0,16 0,07 0,02 0,51 0,54 22,10 62,13 7,70 Slovenia SL 4722,54 0,08 1,05 1,74 1,38 0,16 1,59 1,37 0,15 2,29 0,15 0,00 0,02 0,13 0,14 0,05 0,15 0,16 35,88 52,82 0,70 Slovakia SK 8662,56 0,88 2,81 0,69 0,16 0,03 0,12 2,30 0,14 1,35 0,22 0,02 0,14 0,21 0,14 0,06 0,28 0,45 47,45 41,71 0,83 United-Kingdom UK 51843,40 0,29 4,72 3,83 1,21 0,12 0,83 4,07 0,18 2,65 0,23 0,13 0,17 0,42 0,14 0,11 1,64 2,30 66,81 9,28 0,87 Average 1,27 2,67 1,65 0,99 0,34 0,74 2,89 0,19 2,28 0,20 0,17 0,23 0,34 0,19 0,14 0,68 0,66 55,16 27,01 2,18 Page 85/96

86 Area percentage by Country level 2 Countries Area (km²) Austria AT 11426,13 6,77 4,99 0,52 1,28 52,25 32,80 1,39 Belgium BE 5590,52 19,65 12,60 0,86 2,63 48,73 13,57 1,96 Bulgaria BG 10209,32 4,60 4,58 0,45 0,69 60,80 26,96 1,93 Cyprus CY 2710,23 4,72 3,06 0,75 0,29 90,71 0,31 0,15 Czech Republic CZ 28295,68 5,75 4,36 0,55 1,13 53,28 33,93 1,01 Germany DE ,46 7,89 6,81 0,66 1,94 53,38 27,61 1,72 Denmark DK 17188,46 8,22 5,08 0,39 1,72 69,10 13,63 1,86 Estonia EE 7336,77 3,37 2,37 0,39 0,44 40,57 51,69 1,16 Spain ES 47965,42 4,63 5,39 1,19 0,89 69,78 17,37 0,74 Finland FI 11008,48 6,10 4,09 0,59 1,44 24,74 56,30 6,75 France FR 61064,88 9,11 6,82 0,57 1,60 57,36 23,16 1,38 Greece GR 9563,02 7,42 6,25 0,72 0,69 72,48 11,70 0,74 Hungary HU 12028,59 9,29 5,16 0,70 1,00 60,97 21,49 1,39 Ireland IE 12746,69 6,48 3,85 0,75 1,80 75,70 9,05 2,36 Italia IT 35072,33 8,80 7,99 0,81 1,07 61,73 17,57 2,04 Lithuania LT 8095,35 4,64 2,95 0,43 1,02 50,86 38,04 2,06 Luxembourg LU 2596,59 4,76 5,50 0,44 0,72 51,60 36,51 0,48 Latvia LV 9044,75 3,28 2,73 0,33 0,76 38,57 50,86 3,47 Malta MT 315,58 14,91 14,79 1,81 1,22 66,64 0,05 0,58 Netherlands NL 7288,39 13,01 13,84 1,18 4,62 47,73 11,48 8,15 Poland PL 68644,09 6,03 3,80 0,42 0,91 57,22 29,92 1,70 Portugal PT 5469,78 12,90 8,65 1,35 1,55 49,65 22,48 3,42 Romania RO 5111,96 8,79 7,28 0,65 0,79 63,19 16,88 2,42 Suede SE 41306,67 4,15 2,61 0,26 1,05 22,10 62,13 7,70 Slovenia SL 4722,54 6,00 3,98 0,32 0,30 35,88 52,82 0,70 Slovakia SK 8662,56 4,69 4,17 0,41 0,74 47,45 41,71 0,83 United-Kingdom UK 51843,40 10,98 7,43 0,68 3,94 66,81 9,28 0,87 Average 7,66 5,97 0,67 1,34 55,16 27,01 2,18 Page 86/96

87 Area percentage by Country level 1 Countries Area (km²) Austria AT 11426,13 13,56 52,25 32,80 1,39 Belgium BE 5590,52 35,74 48,73 13,57 1,96 Bulgaria BG 10209,32 10,32 60,80 26,96 1,93 Cyprus CY 2710,23 8,83 90,71 0,31 0,15 Czech Republic CZ 28295,68 11,78 53,28 33,93 1,01 Germany DE ,46 17,29 53,38 27,61 1,72 Denmark DK 17188,46 15,42 69,10 13,63 1,86 Estonia EE 7336,77 6,57 40,57 51,69 1,16 Spain ES 47965,42 12,10 69,78 17,37 0,74 Finland FI 11008,48 12,21 24,74 56,30 6,75 France FR 61064,88 18,09 57,36 23,16 1,38 Greece GR 9563,02 15,08 72,48 11,70 0,74 Hungary HU 12028,59 16,15 60,97 21,49 1,39 Ireland IE 12746,69 12,89 75,70 9,05 2,36 Italia IT 35072,33 18,66 61,73 17,57 2,04 Lithuania LT 8095,35 9,04 50,86 38,04 2,06 Luxembourg LU 2596,59 11,42 51,60 36,51 0,48 Latvia LV 9044,75 7,11 38,57 50,86 3,47 Malta MT 315,58 32,73 66,64 0,05 0,58 Netherlands NL 7288,39 32,65 47,73 11,48 8,15 Poland PL 68644,09 11,16 57,22 29,92 1,70 Portugal PT 5469,78 24,45 49,65 22,48 3,42 Romania RO 5111,96 17,51 63,19 16,88 2,42 Suede SE 41306,67 8,07 22,10 62,13 7,70 Slovenia SL 4722,54 10,60 35,88 52,82 0,70 Slovakia SK 8662,56 10,01 47,45 41,71 0,83 United-Kingdom UK 51843,40 23,04 66,81 9,28 0,87 Average 15,65 55,16 27,01 2,18 Page 87/96

88 Appendix 4 : LUZ Delivery Report - Hamburg example LUZ GENERAL INFORMATION Area of product Format (geodatabase esri) SOIL SEALING cover and projection COTS validation HAMBURG_DE 7206,95 km² Ok Ok Ok Coordinate system reference Projection: UTM 32 Central Meridian: 9 Latitude of Origin: 0 False Easting: ,00 False Northing: 0,00 Ellipsoid: WGS84 IMAGE data used Satellite & Sensor Image Name Date (y/m/d) Remark (e.g. clouds) Spot 5 2,50 m B8 (PS) 2010/06/17 Pan-sharpening B0 (PS) 2009/08/ B0 (PS) 2009/08/ B0 (PS) 2009/08/ B0 (PS) 2009/09/ B0 (PS) 2009/09/01 RapidEye 5 m T110514_RE2_3A-NAC_ _ (XS) 2009/04/ T111046_RE5_3A-NAC_ _ (XS) 2009/04/ T111047_RE5_3A-NAC_ _ (XS) 2009/04/ T111049_RE5_3A-NAC_ _ (XS) 2009/04/ T111050_RE5_3A-NAC_ _ (XS) 2009/04/ T111050_RE5_3A-NAC_ _ (XS) 2009/04/ T111052_RE5_3A-NAC_ _ (XS) 2009/04/ T111053_RE5_3A-NAC_ _ (XS) 2009/04/ T111354_RE1_3A-NAC_ _ (XS) 2009/04/ T111358_RE1_3A-NAC_ _ (XS) 2009/04/ T111747_RE1_3A-NAC_ _ (XS) 2009/05/ T111207_RE3_3A-NAC_ _ (XS) 2009/05/ T111211_RE3_3A-NAC_ _ (XS) 2009/05/ T111213_RE3_3A-NAC_ _ (XS) 2009/05/ T111214_RE3_3A-NAC_ _ (XS) 2009/05/ T111217_RE3_3A-NAC_ _ (XS) 2009/05/ T111221_RE3_3A-NAC_ _ (XS) 2009/05/ T111327_RE5_3A-NAC_ _ (XS) 2009/05/ T111330_RE5_3A-NAC_ _ (XS) 2009/05/ T111330_RE5_3A-NAC_ _ (XS) 2009/05/ T110649_RE5_3A-NAC_ _ (XS) 2009/07/ T110652_RE5_3A-NAC_ _ (XS) 2009/07/ T110653_RE5_3A-NAC_ _ (XS) 2009/07/ T110656_RE5_3A-NAC_ _ (XS) 2009/07/ T112955_RE1_3A-NAC_ _ (XS) 2009/07/ T111044_RE5_3A-NAC_ _ (XS) 2009/07/27 Images created by SPOT IMAGE Page 88/96

89 Ancillary data used (thematic data, satellite images, aerial photos, city maps) Id. Data source/type Title (if relevant) Production date (y/m/d) Scale (spatial detail) Google Earth City map Falk Hamburg 1 : 39000e Remarks CAPI/ DIGITAL CLASSIFICATION Photointerpreter(s) Name/surname Bonnemains diane Sladkowski audrey Bitschene antoine Lecocq aurelien Prevost aurelien Stievenard christophe Auger emeric Fretin david Cornuet jeanne interpretation start (y/m/d) end (y/m/d) Remarks 11/07/21 11/08/19 - Rapideye source image used under the clouds on the other datas - Airbus factory and runway test, not an airport but N / E - old burying ground, now weather station area N / E Internal quality control Internal quality Date of control Result Remarks (errors, corrections, etc.) check (y/m/d) Courmont Yoann 11/08/22 OK Helgoland island (80km from the border of the rest of the LUZ) with no images code N / E Quality assesment Internal quality check Date of control (y/m/d) Result 11/08/23 ok IGNFI Remarks (errors, corrections, etc.) CONFUSION MATRIX Urban strata accuracy calculation The sample population is that of the entire LUZ and non urban classes are aggregated in class 600. The main advantage of this method is to provide both omission and commission errors on the urban strata (omission - commission class 1xx / class 600). Page 89/96

90 IFI Total SIRS % 7.0% % 100.0% % 21.0% % 0.0% % 47.1% % 0.0% % 15.4% % 0.0% % 70.0% % 17.2% % 7.9% % 6.8% Total Producer accuracy 94.2% 0.0% 78.1% 60.0% 90.0% 77.8% 100.0% 42.9% 60.0% 61.5% 89.7% 96.5% Omission error 5.8% 100.0% 21.9% 40.0% 10.0% 22.2% 0.0% 57.1% 40.0% 38.5% 10.3% 3.5% Producer accuracy Commission error Overall accuracy = 89.4% CONFUSION MATRIX (URBAN) Page 90/96

91 IFI Total Producer accuracy Commission error SIRS % 2.1% % 3.6% % 22.3% % 11.8% Total Producer accuracy 96.0% 95.4% 93.5% 96.8% Omission error 4.0% 4.6% 6.5% 3.2% Overall accuracy = 95.7% CONFUSION MATRIX (RURAL) Page 91/96

92 IFI Total Producer accuracy Commission error SIRS % 7.0% % 100.0% % 21.0% % 0.0% % 47.1% % 0.0% % 15.4% % 0.0% % 70.0% % 17.2% % 7.9% % 3.6% % 22.3% % 11.8% Total Producer accuracy 94.2% 0.0% 78.1% 60.0% 90.0% 77.8% 100.0% 42.9% 60.0% 61.5% 89.7% 95.4% 93.5% 96.8% Omission error 5.8% 100.0% 21.9% 40.0% 10.0% 22.2% 0.0% 57.1% 40.0% 38.5% 10.3% 4.6% 6.5% 3.2% Overall accuracy = 88.9% CONFUSION MATRIX (OVERALL) Page 92/96

93 TOPOLOGICAL QUALITY CHECK The topological quality check of all of the LUZs was based on the same rules (see diagramme). Thematic QC on the CAPI phase; Creation of Arcinfo s Topology; Geometric check and validation of Geodatabase s Topology. However, in some cases, especially in large LUZs, the geometric check and arcgis s topology failed. The main cause was the size and shape of a very large and complex polygon composed of roads (code 12220). So, to avoid this problem, SIRS created a 10 km size grid on these LUZs which split this large polygon into smaller ones. Only the code roads are split using this method. After this operation, the geometric and topology checks passed successfully. Internal quality check Date of control (y/m/d) Result Remark (errors, corrections, etc.) SIRS 11/08/31 ok Page 93/96

94 Page 94/96