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A transferable deep learning framework for flood mapping: Spatial generalization across hydro-climatic regimes using satellite imagery
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DOI：10.1016/j.jag.2026.105321.png)
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Abstract
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• Two U-Net models trained on Sentinel-1 and Sentinel-2 imagery were developed for transferable water segmentation. • Hydro-climatic similarity analyses were introduced to evaluate representativeness and guide model transferability. • Structural biases were systematically profiled to inform model design. • A Leave-One-Location-Out cross-validation was implemented to measure true spatial generalization across 18 flood events. • Sensor-specific patterns revealed complementary strengths of radar and optical data for operational flood mapping.
Keywords:
Flood mapping
Deep learning
Operational readiness
U-Net
Sentinel-1
Sentinel-2
Spatial generalization
Domain shift
Leave-One-Location-Out cross-validation
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