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Published September 4, 2023 | Version v1.0.0
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S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images

  • 1. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR)
  • 2. German Research Centre for Geosciences (GFZ)

Description

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 water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.

This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. 

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Additional details

Related works

Is described by
Publication: 10.1109/JSTARS.2023.3333969 (DOI)
Is supplemented by
Software: https://github.com/MWieland/s1s2_water/tree/main (URL)

References

  • Wieland, M., Fichtner, F., Martinis, S., Groth, S., Krullikowski, C., Plank, S., Motagh, M. (2023). S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2023.3333969.