DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization
arXiv:2504.11637 · doi:10.1016/j.ijdrr.2025.105704
Abstract
Rapid, accurate, and descriptive building damage assessment is critical for directing post-disaster resources, yet current automated methods typically provide only binary (damaged/undamaged) or ordinal severity scales. This paper introduces DamageCAT, a framework that advances damage assessment through typology-based categorical classifications. We contribute: (1) the BD-TypoSAT dataset containing satellite image triplets from Hurricane Ida with four damage categories - partial roof damage, total roof damage, partial structural collapse, and total structural collapse - and (2) a hierarchical U-Net-based transformer architecture for processing pre- and post-disaster image pairs. Our model achieves 0.737 IoU and 0.846 F1-score overall, with cross-event evaluation demonstrating transferability across Hurricane Harvey, Florence, and Michael data. While performance varies across damage categories due to class imbalance, the framework shows that typology-based classifications can provide more actionable damage assessments than traditional severity-based approaches, enabling targeted emergency response and resource allocation.
26 pages, 13 figures
References in corpus (5)
- Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion
- SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images
- DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery
- BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge
- Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach