DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization
This work addresses the need for more descriptive damage assessments to improve post-disaster resource allocation, though it is incremental as it builds on existing U-Net and transformer methods.
The paper tackles the problem of automated building damage assessment by introducing DamageCAT, a framework for typology-based categorical classifications using satellite images, achieving an overall IoU of 0.737 and F1-score of 0.846.
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.