Flood Mapping from RGB imagery using a Vision Foundation Model
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost. To produce flood maps, deep learning models for water segmentation are often used. CNN based and small vision transformer models are used. However, they need much data for adaptation to a change of scenery, i.e., another flooding event. Vision foundation models or large vision transformers are known to generalize across domains. Recently, foundation models for Earth observation became available. They are pretrained on satellite data, whose spatial resolution, viewing geomet...
| Authors | Vladyslav Polushko, Tilman Bucher, Ronald Rösch et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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