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S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images

The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of

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cc-by-4.0
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16/10/2026
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Provenance & evidence

Listed in zenodo public catalogue API on 2026-10-16; relevance/value screened (haiku).

Metadata observed 14/10/2026 · research sweep: sweep-2026-10-16 (zenodo)