Mapping global floods with 10 years of satellite radar data
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| Publicado en: | Nature Communications vol. 16, no. 1 (2025), p. 5762 |
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| Autor Principal: | |
| Outros autores: | , , , |
| Publicado: |
Nature Publishing Group
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| Materias: | |
| Acceso en liña: | Citation/Abstract Full Text Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 2041-1723 | ||
| 024 | 7 | |a 10.1038/s41467-025-60973-1 |2 doi | |
| 035 | |a 3226269274 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 145839 |2 nlm | ||
| 100 | 1 | |a Misra, Amit |u Microsoft AI for Good Research Lab, Redmond, USA | |
| 245 | 1 | |a Mapping global floods with 10 years of satellite radar data | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Floods cause extensive global damage annually, making effective monitoring essential. While satellite observations have proven invaluable for flood detection and tracking, comprehensive global flood datasets spanning extended time periods remain scarce. In this study, we introduce a deep learning flood detection model that leverages the cloud-penetrating capabilities of Sentinel-1 Synthetic Aperture Radar (SAR) satellite imagery, enabling consistent flood extent mapping through cloud cover and in both day and night conditions. By applying this model to 10 years of SAR data, we create a unique, longitudinal global flood extent dataset with predictions unaffected by cloud coverage, offering comprehensive and consistent insights into historically flood-prone areas over the past decade. We use our model predictions to identify historically flood-prone areas in Ethiopia and demonstrate real-time disaster response capabilities during the May 2024 floods in Kenya. Additionally, our longitudinal analysis reveals potential increasing trends in global flood extent over time, although further validation is required to explore links to climate change. To maximize impact, we provide public access to both our model predictions and a code repository, empowering researchers and practitioners worldwide to advance flood monitoring and enhance disaster response strategies.Analysis of 10 years of satellite radar data with a deep learning model reveals historical flood patterns often missed in prior datasets. This dataset also enables analysis of trends in flooding, showing hints of increases in flood extent over time. | |
| 651 | 4 | |a Europe | |
| 653 | |a Climate change | ||
| 653 | |a Surface water | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Flood damage | ||
| 653 | |a Trends | ||
| 653 | |a Radar | ||
| 653 | |a Radar data | ||
| 653 | |a Floods | ||
| 653 | |a Cloud cover | ||
| 653 | |a Flood mapping | ||
| 653 | |a Satellite imagery | ||
| 653 | |a Historic floods | ||
| 653 | |a Disaster management | ||
| 653 | |a Radar imaging | ||
| 653 | |a Monitoring | ||
| 653 | |a Disasters | ||
| 653 | |a Predictions | ||
| 653 | |a Synthetic aperture radar | ||
| 653 | |a Sensors | ||
| 653 | |a Maps | ||
| 653 | |a Mapping | ||
| 653 | |a Emergency communications systems | ||
| 653 | |a Archives & records | ||
| 653 | |a Satellite observation | ||
| 653 | |a Public access | ||
| 653 | |a Real time | ||
| 653 | |a Satellites | ||
| 653 | |a Flood predictions | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a White, Kevin |u Microsoft AI for Good Research Lab, Redmond, USA | |
| 700 | 1 | |a Nsutezo, Simone Fobi |u Microsoft AI for Good Research Lab, Redmond, USA | |
| 700 | 1 | |a Straka, William |u University of Wisconsin-Madison, Cooperative Institute for Meteorological Satellite Studies, Madison, USA (GRID:grid.14003.36) (ISNI:0000 0001 2167 3675) | |
| 700 | 1 | |a Lavista, Juan |u Microsoft AI for Good Research Lab, Redmond, USA (GRID:grid.14003.36) | |
| 773 | 0 | |t Nature Communications |g vol. 16, no. 1 (2025), p. 5762 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3226269274/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3226269274/fulltext/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3226269274/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |