S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Marc Wieland
  • Florian Fichtner
  • Sandro Martinis
  • Sandro Groth
  • Christian Krullikowski
  • Simon Plank
  • Mahdi Motagh

External Research Organisations

  • German Aerospace Center (DLR)
  • Helmholtz Centre Potsdam - German Research Centre for Geosciences (GFZ)
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Details

Original languageEnglish
Pages (from-to)1084-1099
Number of pages16
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume17
Publication statusPublished - 17 Nov 2023

Abstract

This study introduces the S1S2-Water dataset - a global reference dataset for training, validation, and testing of convolutional neural networks (CNNs) 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 km × 100 km) under consideration of predominant landcover and availability of water bodies. Each sample is complemented with metadata and digital elevation model (DEM) raster from the Copernicus DEM. On the basis of this dataset, we carry out performance evaluation of CNN architectures to segment surface water bodies from Sentinel-1 and Sentinel-2 images. We specifically evaluate the influence of image bands, elevation features (slope) and data augmentation on the segmentation performance and identify best-performing baseline-models. The model for Sentinel-1 achieves an Intersection over Union (IoU) of 0.845, Precision of 0.932, and Recall of 0.896 on the test data. For Sentinel-2 the best model produces an IoU of 0.965, Precision of 0.989, and Recall of 0.951, respectively. We also evaluate the performance impact when a model is trained on permanent water data and applied to independent test scenes of floods.

Keywords

    Convolutional neural networks (CNNs), reference dataset, semantic segmentation, Sentinel-1, Sentinel-2, surface water monitoring

ASJC Scopus subject areas

Cite this

S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images. / Wieland, Marc; Fichtner, Florian; Martinis, Sandro et al.
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 17, 17.11.2023, p. 1084-1099.

Research output: Contribution to journalArticleResearchpeer review

Wieland M, Fichtner F, Martinis S, Groth S, Krullikowski C, Plank S et al. 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. 2023 Nov 17;17:1084-1099. doi: 10.1109/JSTARS.2023.3333969
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title = "S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images",
abstract = "This study introduces the S1S2-Water dataset - a global reference dataset for training, validation, and testing of convolutional neural networks (CNNs) 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 km × 100 km) under consideration of predominant landcover and availability of water bodies. Each sample is complemented with metadata and digital elevation model (DEM) raster from the Copernicus DEM. On the basis of this dataset, we carry out performance evaluation of CNN architectures to segment surface water bodies from Sentinel-1 and Sentinel-2 images. We specifically evaluate the influence of image bands, elevation features (slope) and data augmentation on the segmentation performance and identify best-performing baseline-models. The model for Sentinel-1 achieves an Intersection over Union (IoU) of 0.845, Precision of 0.932, and Recall of 0.896 on the test data. For Sentinel-2 the best model produces an IoU of 0.965, Precision of 0.989, and Recall of 0.951, respectively. We also evaluate the performance impact when a model is trained on permanent water data and applied to independent test scenes of floods.",
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