A MUST-Former: A Family of Transformer-Based Models for Robust Multi-Modal Flood Mapping from Satellite Data.

Abstract

Flood disasters pose severe threats to human lives and infrastructure worldwide. Accurate and timely flood detection using satellite imagery is essential for emergency response and disaster management. This paper introduces MUST-Former, a family of five deep learning models designed for robust global flood mapping using multi-modal satellite data. The proposed architectures leverage Sentinel-1 radar and Sentinel-2 optical imagery from the Sen1Floods11 dataset, which covers 11 flood-affected regions across 6 continents. Three distinct fusion strategies are presented: Projection-based fusion, Cross-Attention fusion, and the flagship Principal Component Analysis-inspired fusion. The models address critical challenges in flood detection, including severe class imbalance, geographic bias, and sensor-specific limitations. The MUST-Former-PCA variant achieves a mean Intersection over Union of 90.35% on the test set and 89.32% on the unseen Bolivia region, demonstrating superior generalization. Compared to existing baselines, MUST-Former-SAR shows a 160% improvement in mean IoU over the original Sen1Floods11 baseline, while MUST-Former-Optical achieves competitive performance against foundation models using 3.6 times fewer parameters and superior geographic generalization on the unseen Bolivia split. The fusion models successfully mitigate single-modality failures across diverse geographic regions. Statistical reproducibility is confirmed across three independent random seeds. These results prove the effectiveness of multi-modal approaches for operational flood mapping systems.