mlsanityData to 14 Oct 2026

Geospatial Artificial Intelligence for Satellite-Based Flood Extent Mapping: Concepts, Advances, and Future Perspectives

arXiv 2504.02214 ↗First posted 3 Apr 2025cs.CV eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 73 references that are in the map. First posted on arXiv 3 Apr 2025.

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22 of 73 references.

References · 22

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10 more
  1. 2024Application-Driven Innovation in Machine Learningcited by 24
  2. 2023Kuro Siwo: 33 billion m2 under the water. A global multi-temporal satellite dataset for rapid flood mappingcited by 16
  3. 2023Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detectioncited by 14
  4. 2023Assessment of a new GeoAI foundation model for flood inundation mappingcited by 14
  5. 2020H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinementcited by 12
  6. 2024UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mappingcited by 11
  7. 2017Exploiting ConvNet Diversity for Flooding Identificationcited by 7
  8. 2023Cross Modal Distillation for Flood Extent Mappingcited by 6
  9. 2024Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagerycited by 6
  10. 2023Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagerycited by 1

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