DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization

• URL: http://arxiv.org/abs/2504.11637v2
• Date: Fri, 08 Aug 2025 20:48:22 GMT
• Title: DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization
• Abstract summary: This paper introduces DamageCAT, a framework that advances damage assessment through typology-based categorical classifications.<n>We contribute: (1) the BD-TypoSAT dataset containing satellite image triplets from Hurricane Ida with four damage categories; and (2) a hierarchical U-Net-based transformer architecture for processing pre- and post-disaster image pairs.<n>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.
• Score: 1.9835707645687721
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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