Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery
arXiv:2606.26204v1 Announce Type: new Abstract: 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,
The expanding availability of satellite data and advancements in AI, specifically deep learning, are enabling more robust and accurate environmental monitoring solutions, including flood detection.
Accurate and rapid flood detection is crucial for emergency response, humanitarian aid, and mitigating economic losses, which will become more frequent with changing climate patterns.
The ability to combine optical and radar imagery with AI improves flood detection accuracy and resilience to cloud cover, enhancing disaster management capabilities globally.
- · Emergency response agencies
- · Satellite imagery providers
- · AI developers
- · Insurance industry
- · Regions without robust monitoring infrastructure
- · Traditional flood modeling techniques
Improved flood detection leads to quicker response times and reduced damage.
Better flood mapping enhances urban planning and infrastructure development in at-risk areas.
The demonstrated efficacy of AI in environmental monitoring accelerates its adoption in related fields, driving further innovation and investment in climate resilience technologies.
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Read at arXiv cs.LG