DamageCAT: A deep learning transformer framework for typology-based post-disaster building damage categorization

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 classification can provide more actionable damage assessments than traditional severity-based approaches, enabling targeted emergency response and resource allocation.