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A new remote sensing benchmark dataset for machine learning applications : MultiSenGE

<p>[UPDATE] You can now access MultiSen (GE and NA) collection though this portal : <a href="https://doi.theia.data-terra.org/ai4lcc/?lang=en">https://doi.theia.data-terra.org/ai4lcc/?lang=en</a></p> <p>MultiSenGE is a new large-scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. It contains 8,157 patches of 256 * 256 pixels for Sentinel-2 L2A, Sentinel-1 GRD and a regional LULC topographic regional database.&nbsp;</p> <p>Every file has a specific nomenclature :</p> <ul> <li>Sentinel-1 patches:&nbsp;{tile}_{date}_S1_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Sentinel-2 patches:&nbsp;{tile}_{date}_S2_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Ground reference patches:&nbsp;{tile}_GR_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>JSON Labels:&nbsp;{tile}_{x-pixel-coordinate}_{y-pixel-coordinate}.json</li> </ul> <p>where <em>tile</em> is the Sentinel-2 tile number, <em>date</em> the date of acquisition of the patch,&nbsp;<em>x-pixel-coordinate</em> and&nbsp;<em>y-pixel-coordinate</em> are the coordinates of the patch in the tile.</p> <p>In addition, you can find a set of useful python tools for extracting information about the dataset on Github :&nbsp;<a href="https://github.com/r-wenger/MultiSenGE-Tools">https://github.com/r-wenger/MultiSenGE-Tools</a></p> <p>First experiments based on this <em>dataset</em> is in press&nbsp;in&nbsp;ISPRS Annals&nbsp;: <strong>Wenger, R.,&nbsp;</strong>Puissant, A., Weber, J., Idoumghar, L., and Forestier, G.: MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 635&ndash;640, https://doi.org/10.5194/isprs-annals-V-3-2022-635-2022, 2022.</p> <p>Due to the large size of the dataset, you will only find the associated JSON files on this Zenodo repository. To download the Sentinel-1, Sentinel-2 patches and the reference data, please do so via these links:&nbsp;</p> <ul> <li>Sentinel-1 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz</a></li> <li>Sentinel-2 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz</a></li> <li>Ground reference patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz</a></li> <li>JSON files for each patch: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz</a></li> </ul>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
8
Engagement
4

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