ussol_2017I_68634 eng 8859part1 Layer Layer-sheet Generalitat de Catalunya https://territori.gencat.cat/ https://territori.gencat.cat/ca/01_departament/12_cartografia_i_toponimia/bases_cartografiques/medi_ambient_i_sostenibilitat/usos-del-sol/ Unknown Processor 2022-03-17 ISO 19115:2003/Cor.1:2006 Geographic Information - Metadata IS 2 Row 25949 10 Column 26940 10 Area true false 260165 4747975 529555 4488495 center http://www.opengis.net/def/crs/EPSG/0/25831 Catalonia 2017 v.3 Land Use/Land Cover Map Catalonia 2017 v.3 Land Use/Land Cover Map 2019-01-16 Creation 2019-01-16 Revision 2019-01-16 ussol_2017I_68634_v3 2017 Land use and land cover map of Catalonia obtained through automatic classification of Sentinel-2A and Sentinel-2B images with a spatial resolution of 10 m and auxiliary information from the Urbanistic Map and the Road Map as well as the forest fire mapping base of Catalonia, provided by the Generalitat de Catalunya, and lidar data from the Cartographic and Geological Institute of Catalonia (ICGC). In version 2, the representation of some categories in the south of Catalonia was improved, especially conifer forests. Version 3 improves some points of the classification in urban and industrial or commercial areas. This base is the original at 10 m. Analysis of land use and land cover changes in Catalonia between 2012 and 2017 and study of the speeds and accelerations of change in the period 1987-2017. Grup de Recerca en Teledetecció i Sistemes d'Informació Geogràfica [Grumets, CREAF-UAB] https://www.grumets.cat/ https://www.grumets.cat/ Information Author Generalitat de Catalunya https://www.gencat.cat https://www.gencat.cat Information Owner Xavier Pons Grup de Recerca en Teledetecció i Sistemes d'Informació Geogràfica [Grumets, CREAF-UAB] Principal Investigator Principal investigator soil use theme GEMET Thesaurus version 1.0 2008-06-10 Revision European Environment Information and Observation Network (Eionet) https://www.eionet.europa.eu/gemet/ Originator land cover theme GEMET Thesaurus version 1.0 2008-06-10 Revision European Environment Information and Observation Network (Eionet) https://www.eionet.europa.eu/gemet/ Originator remote sensing theme GEMET Thesaurus version 1.0 2008-06-10 Revision European Environment Information and Observation Network (Eionet) https://www.eionet.europa.eu/gemet/ Originator mapping theme GEMET Thesaurus version 1.0 2008-06-10 Revision European Environment Information and Observation Network (Eionet) https://www.eionet.europa.eu/gemet/ Originator Catalonia place MiraMon Thesaurus 2021-05-25 Revision MiraMon https://www.miramon.cat Information Originator Grid 25000 10 English farming biota imageryBaseMapsEarthCover inlandWaters true Catalunya true 0.0648605767954534 3.36198094989502 40.5124741400814 42.8838480920365 The values of the geometric quality documented in this metadata has been calculated by placing test points on the current ICGC orthophoto (2016-2018) at a scale of 1:5000. CLASSIF_2017: (byte-RLE) Categorical thematic classification (e.g., land cover map) CLASSIF_2017 CampGeneric Generalitat de Catalunya Distributor MMZX 1 0 ISO 19165-2:2020 ZIP Catalunya 33 https://territori.gencat.cat/ca/01_departament/12_cartografia_i_toponimia/bases_cartografiques/medi_ambient_i_sostenibilitat/usos-del-sol/ https Standard MMZX (ISO 19165) and GeoTIFF downloads Download Layer RMS (independent test) Root Mean Square Error Positional accuracy:Relative horizontal accuracy:RMS (independent test) Double precision real Root Mean Square Error 2D 3.7747 RMS on X (independent test) Root Mean Square Error X Positional accuracy:Relative horizontal accuracy:RMS on X (independent test) Double precision real Root Mean Square Error 5.6887 RMS on Y (independent test) Root Mean Square Error Y Positional accuracy:Relative horizontal accuracy:RMS on Y (independent test) Double precision real Root Mean Square Error 3.8078 González-Guerrero, O, Pons X (2020) The 2017 Land Use/Land Cover Map of Catalonia based on Sentinel-2 images and auxiliary data. Revista de Teledetección, 55: 81-92. https://doi.org/10.4995/raet.2020.13112 Attribute Global accuracy (without taking into account unclassified pixels) Misclassification,PredictedValues/ActualValues Semantic accuracy (categorical data):Accuracy of thematic classification:Global accuracy (without taking into account unclassified pixels) Double precision real Overall Accuracy 98.66 Global accuracy Misclassification,PredictedValues/ActualValues Semantic accuracy (categorical data):Accuracy of thematic classification:Global accuracy Double precision real Overall Accuracy 98.55 Weighted global accuracy based on the area of the evaluated layer Misclassification,PredictedValues/ActualValues Semantic accuracy (categorical data):Accuracy of thematic classification:Weighted global accuracy based on the area of the evaluated layer Double precision real Overall Accuracy 96.69 Weighted global accuracy based on the area of the evaluated layer (only classified pixels) Misclassification,PredictedValues/ActualValues Semantic accuracy (categorical data):Accuracy of thematic classification:Weighted global accuracy based on the area of the evaluated layer (only classified pixels) Double precision real Overall Accuracy 98.13 Weighted global accuracy based on the ground truth area Misclassification,PredictedValues/ActualValues Semantic accuracy (categorical data):Accuracy of thematic classification:Weighted global accuracy based on the ground truth area Double precision real Overall Accuracy 98.12 Weighted global accuracy based on the ground truth area (only classified pixels) Misclassification,PredictedValues/ActualValues Semantic accuracy (categorical data):Accuracy of thematic classification:Weighted global accuracy based on the ground truth area (only classified pixels) Double precision real Overall Accuracy 98.12