Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery
Floods frequently impact regions around the world. Rapid and accurate flood detection is crucial for emergency response and timely mitigation of human and economic loss. The expanding availability of satellite data and advances in artificial intelligence have enhanced monitoring of environmental hazards, but many flood events remain challenging to detect because cloud cover obscures optical satellite imagery. Rambour et al. introduced the SEN12-FLOOD dataset and extracted per-image features using a ResNet-50 convolutional neural network backbone, then fed these features into a gated recurrent unit network to show that temporal information can substantially improve accuracy compared to sin...
| Authors | Sophia Li, Max Zhao, Raghu G. Raj et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 June 2026 |
| Last updated at source | 26 June 2026 |
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