Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation

• URL: http://arxiv.org/abs/2603.14694v1
• Date: Mon, 16 Mar 2026 01:03:30 GMT
• Title: Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation
• Abstract summary: Models trained on multi-disaster benchmarks often underperform in unseen geographic regions due to domain shift.<n>We explore a two-stage ensemble approach using supervised domain adaptation for building damage classification across four severity classes.<n>Our pipeline achieves the most robust performance using SDA with unsharp-enhanced RGB input, attaining a Macro-F1 of 0.5552.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Rapid structural damage assessment from remote sensing imagery is essential for timely disaster response. Within human-machine systems (HMS) for disaster management, automated damage detection provides decision-makers with actionable situational awareness. However, models trained on multi-disaster benchmarks often underperform in unseen geographic regions due to domain shift - a distributional mismatch between training and deployment data that undermines human trust in automated assessments. We explore a two-stage ensemble approach using supervised domain adaptation (SDA) for building damage classification across four severity classes. The pipeline adapts the xView2 first-place method to the Ida-BD dataset using SDA and systematically investigates the effect of individual augmentation components on classification performance. Comprehensive ablation experiments on the unseen Ida-BD test split demonstrate that SDA is indispensable: removing it causes damage detection to fail entirely. Our pipeline achieves the most robust performance using SDA with unsharp-enhanced RGB input, attaining a Macro-F1 of 0.5552. These results underscore the critical role of domain adaptation in building trustworthy automated damage assessment modules for HMS-integrated disaster response.

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