Deep Learning for Flood Risk Mapping with Sentinel‑2 in Python

You can start with minimal remote sensing knowledge if you understand the basics of raster data and are careful with band selection and normalization. However, domain intuition becomes important when you debug failure modes (cloud shadows, urban darkness, wetlands) and when you translate masks into risk decisions.

Yes, but you should expect weaker generalization across geographies. A common approach is to start from a pre-trained segmentation backbone (or a model trained on a global dataset) and fine-tune on your region, while keeping evaluation stratified across land cover types.

Spectral indices are strong baselines and should remain part of your toolbox, but they can produce false positives in shadows/dark surfaces and degrade under common flood conditions (clouds, turbidity). Deep learning helps by learning spatial context and richer cross-band patterns.

Don’t hide them. Predict clouds explicitly (or use reliable cloud masks), and carry an “unobservable” layer through your pipeline. In reporting, distinguish “not flooded” from “cannot observe due to cloud.”