Explainable Flood Segmentation on Sentinel-1 SAR1 Imagery Using CNN and Transformer Architectures
Rapid and accurate flood prediction is essential for disaster response and mitigation planning. Synthetic Aperture Radar (SAR) sensors in satellites are well-suited for this purpose because they operate independently of weather and daylight conditions. Although SAR-based data enable all-weather flood monitoring, distinguishing flooded land from permanent water remains a significant challenge, particularly when flooding is defined strictly as inundated land. This study provides a comprehensive comparison of convolutional neural network (CNN) and vision transformer architectures for multi-class flood segmentation using Sentinel-1 SAR imagery, specifically trained to separate flooded land fr...
| Authors | Arundhuti Banerjee, David Daou |
|---|---|
| Primary category | cs.CV |
| Published | 15 June 2026 |
| Last updated at source | 28 July 2026 |
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