Multiple Land-use / Land-cover Dataset (MLULC)

<p>This dataset covers the French metropolitan territory (500,000km&sup2;). It includes</p> <ul> <li> <p>Six open access land-cover maps from various providers (<a href="https://land.copernicus.eu/pan-european/corine-land-cover">CLC</a>, <a href="https://land.copernicus.eu/global/products/lc">CGLS-LC100</a>, <a href="https://theia.cnes.fr/atdistrib/rocket/#/search?collection=OSO">OSO</a>, <a href="https://geoservices.ign.fr/ocsge">OCS-GE cover</a>, <a href="https://geoservices.ign.fr/ocsge">OCS-GE use</a>, <a href="https://www.data.gou v.fr/en/datasets/mode-doccupation-du-sol-mos-en-11-postes-en-2017/">MOS</a>).</p> </li> <li> <p>&nbsp;A May 2019 Sentinel-2 L3A mosaic&nbsp;( cloudless image using all maps available during a month) including RVB and NIR. Provided by <a href="https://theia.cnes.fr/atdistrib/rocket/#/search?page=1&amp;collection=SENTINEL2&amp;processingLevel=LEVEL3A">Theia</a>.</p> </li> <li> <p>A Manually built ground truth of 2300 random points annotated with their labels in each map nomenclature.</p> </li> <li> <p>A consolidated ground truth with the original 2300 and 400 non-random points focusing on rare classes.</p> </li> <li> <p>A suggested train/val/test split (60%,5%,35%). Note that all ground truth points belong to patches of the suggested <strong>test</strong> set.</p> </li> </ul> <p>Since this dataset is intended to be used with a deep learning algorithm, the data is split into tiles of 6x6km&sup2; following a grid given with the dataset.</p> <p>More information is provided in README.</p> <p>Note that exception made of the ground truth, all the data (Land covers and Sentinel-Images) aren&#39;t our property and are only shared as authorized by their respective original license.</p>