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The availability of curated large-scale training data is a crucial factor forthe development of well-generalizing deep learning methods for the extractionof geoinformation from multi-sensor remote sensing imagery. While quite somedatasets have already been published by the community, most of them suffer fromrather strong limitations, e.g. regarding spatial coverage, diversity or simplynumber of available samples. Exploiting the freely available data acquired bythe Sentinel satellites of the Copernicus program implemented by the EuropeanSpace Agency, as well as the cloud computing facilities of Google Earth Engine,we provide a dataset consisting of 180,662 triplets of dual-pol syntheticaperture radar (SAR) image patches, multi-spectral Sentinel-2 image patches,and MODIS land cover maps. With all patches being fully georeferenced at a 10 mground sampling distance and covering all inhabited continents during allmeteorological seasons, we expect the dataset to support the community indeveloping sophisticated deep learning-based approaches for common tasks suchas scene classification or semantic segmentation for land cover mapping.

SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion