CFP-SwinT: building damage classification during floods using cross fusion pyramid Swin-T

Timely building damage detection is crucial in disaster decision-making. Unlike existing studies using sub-meter resolution images, we employ stable, mid-high resolution (1–3 meters) remote sensing data to develop building flood damage classification methods for quick disaster response. A mid-high-resolution dataset of 973 Planet-sliced images from three floods in different countries is presented to facilitate building damage classification. Based on this, we propose a Cross Fusion Pyramid Swin Transformer (CFP-SwinT), which balances local and global representations in flood scenes. The model employs feature pyramids to classify multi-scale building groups, and integrates spatial-channel features from dual-time images via a cross-fusion block to jointly capture building groups (pre-disaster) and damage severity (post-disaster). Through extensive experiments, CFP-SwinT outperformed all other backbones and settings and achieved 80.2% accuracy on four-level classification, demonstrating its effective focus on multi-scale disaster information and dual-temporal changes. To enable rapid adaptation to different disaster-affected regions, we combined an unsupervised domain adaptation method with CFP-SwinT, allowing a model trained on one disaster to achieve 77.8% accuracy on unseen flood events. This approach avoids sample labeling, which can benefit decision-making efficiency during disasters. The code for the proposed method is available at https://github.com/WuChuYi/Cross-Fusion-Pyramid-Swin-Transformer.