Post-disaster affected area segmentation with vision transformer (ViT)-based model using Sentinel-2 and Formosat-5 imagery

Abstract We propose a vision transformer (ViT)-based deep learning framework to improve disaster-affected area segmentation from satellite images, supporting the Emergent Value Added Product (EVAP) system developed by the Taiwan Space Agency (TASA). The process begins with a small number of manually labeled regions. We then use principal component analysis (PCA) to expand these labels with a confidence interval, creating a weakly supervised training set. Our model, which takes multi-band input from Sentinel-2 and Formosat-5 satellites, is trained to distinguish disaster-affected areas using these expanded labels. We adopt several strategies to increase accuracy when only limited supervision is available. To evaluate performance, our predictions are compared to higher-resolution EVAP results to measure spatial accuracy and consistency. Experiments on real disaster events, such as the 2022 Poyang Lake drought and the 2023 Rhodes wildfire, shows that our approach produces smoother and more reliable segmentation maps, providing a practical solution for disaster mapping when detailed ground truth is lacking.