Geospatial Artificial Intelligence for Satellite-Based Flood Extent Mapping: Concepts, Advances, and Future Perspectives
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908406183362560 |
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| author | Lee, Hyunho Li, Wenwen |
| author_facet | Lee, Hyunho Li, Wenwen |
| contents | Geospatial Artificial Intelligence (GeoAI) for satellite-based flood extent mapping systematically integrates artificial intelligence techniques with satellite data to identify flood events and assess their impacts, for disaster management and spatial decision-making. The primary output often includes flood extent maps, which delineate the affected areas, along with additional analytical outputs such as uncertainty estimation and change detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geospatial Artificial Intelligence for Satellite-Based Flood Extent Mapping: Concepts, Advances, and Future Perspectives Lee, Hyunho Li, Wenwen Computer Vision and Pattern Recognition Image and Video Processing Geospatial Artificial Intelligence (GeoAI) for satellite-based flood extent mapping systematically integrates artificial intelligence techniques with satellite data to identify flood events and assess their impacts, for disaster management and spatial decision-making. The primary output often includes flood extent maps, which delineate the affected areas, along with additional analytical outputs such as uncertainty estimation and change detection. |
| title | Geospatial Artificial Intelligence for Satellite-Based Flood Extent Mapping: Concepts, Advances, and Future Perspectives |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2504.02214 |