Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation
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 ...
| Authors | Asmae Mouradi, Shruti Kshirsagar |
|---|---|
| Primary category | cs.CV |
| Published | 16 March 2026 |
| Last updated at source | 9 May 2026 |
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