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                <gco:CharacterString>This data set is conformant with the INSPIRE Implementing Rules for the interoperability of spatial data sets and services</gco:CharacterString>
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                <gco:Boolean>true</gco:Boolean>
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      <mdq:report>
        <mdq:DQ_DomainConsistency>
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                  <cit:title>
                    <gco:CharacterString>INSPIRE Data Specification on orthoimagery - Guidelines</gco:CharacterString>
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                  <cit:date>
                    <cit:CI_Date>
                      <cit:date>
                        <gco:Date>2010-04-26</gco:Date>
                      </cit:date>
                      <cit:dateType>
                        <cit:CI_DateTypeCode codeList="http://standards.iso.org/iso/19115/resources/Codelists/cat/codelists.xml#CI_DateTypeCode" codeListValue="publication" />
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                <gco:CharacterString>See the referenced specification</gco:CharacterString>
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                <gco:Boolean>true</gco:Boolean>
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        <gco:CharacterString>This product is developed within the Copernicus Global Land Cover and Tropical Forest Mapping and Monitoring service (LCFM), which is part of the Copernicus Land Monitoring Service (CLMS). It is mainly based on data from Copernicus Sentinel-1 and Sentinel-2 satellites, a global training dataset, and several auxiliary layers. The product is generated based on a deep learning algorithm.

The primary input consists of Sentinel-2 L2A optical imagery with surface reflectance data at 10m-20m resolution and Sentinel-1 GRD synthetic aperture radar data, processed to terrain-corrected Gamma0 backscatter. For Sentinel-2, an additional cloud masking is applied based on a deep learning algortihm. For Sentinel-1 data, monthly mosaics are used. Annual features are generated from these input data sources to ensure consistent coverage despite cloud cover and other data gaps.

The classification is supported by an extensive global training database compiled through the Geo-Wiki platform, which contains expert-annotated samples distributed across all biomes and environmental conditions.
Auxiliary datasets integrated in the processing include the Copernicus Digital Elevation Model (providing elevation), OpenStreetMap data (especially for roads) and AgERA5 meteorological data.</gco:CharacterString>
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