Flooding is a major global natural disaster exacerbated by climate change and urbanization.Timely assessment and mapping of inunda-tions are crucial for preventive and emergency measures,driving the demand for curated...Flooding is a major global natural disaster exacerbated by climate change and urbanization.Timely assessment and mapping of inunda-tions are crucial for preventive and emergency measures,driving the demand for curated global geospatial data to implement novel algo-rithms.This study introduces the STURM-Flood dataset,a high-quality,open-access,and DL-ready resource for flood extent mapping using Sentinel-1 and Sentinel-2 satellite imagery,combined with ground-truth data from the Copernicus Emergency Management Service.The dataset encompasses 21,602 Sentinel-1 tiles and 2,675 Sentinel-2 tiles,each measuring 128×128 pixels at 10 m resolution,alongside corre-sponding water masks covering 60 flood events globally.Two U-Net models evaluated the dataset:Sentinel-1 achieved 83.61%test accu-racy and 0.8327 weighted F1-score,while Sentinel-2 yielded 92.75%test accuracy and 0.9243 weighted F1-score.These results underscore the dataset's potential in developing robust models for water extent mapping.STURM-Flood dataset aims to provide a valuable resource for research and development in flood mapping and disaster manage-ment.Future research could focus on expanding and refining different approaches and data sources for broader applications.The reference data and code are openly available in the Zenodo repository https://doi.org/10.5281/zenodo.12748983 and GitHub repository https://github.com/STURM-WEO/STURM-Flood.展开更多
基金funded by the European Union under the Marie Sklodowska-Curie Actions(MSCA)Postdoctoral Fellowships-European Fellowships(Grant agreement ID:101105589).
文摘Flooding is a major global natural disaster exacerbated by climate change and urbanization.Timely assessment and mapping of inunda-tions are crucial for preventive and emergency measures,driving the demand for curated global geospatial data to implement novel algo-rithms.This study introduces the STURM-Flood dataset,a high-quality,open-access,and DL-ready resource for flood extent mapping using Sentinel-1 and Sentinel-2 satellite imagery,combined with ground-truth data from the Copernicus Emergency Management Service.The dataset encompasses 21,602 Sentinel-1 tiles and 2,675 Sentinel-2 tiles,each measuring 128×128 pixels at 10 m resolution,alongside corre-sponding water masks covering 60 flood events globally.Two U-Net models evaluated the dataset:Sentinel-1 achieved 83.61%test accu-racy and 0.8327 weighted F1-score,while Sentinel-2 yielded 92.75%test accuracy and 0.9243 weighted F1-score.These results underscore the dataset's potential in developing robust models for water extent mapping.STURM-Flood dataset aims to provide a valuable resource for research and development in flood mapping and disaster manage-ment.Future research could focus on expanding and refining different approaches and data sources for broader applications.The reference data and code are openly available in the Zenodo repository https://doi.org/10.5281/zenodo.12748983 and GitHub repository https://github.com/STURM-WEO/STURM-Flood.
