ARXIV:2603.14694 · DISASTER RESPONSE · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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

arXiv

Automated damage detection for disaster response using domain adaptation techniques.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain Automated damage detection for disaster response using domain adaptation techniques.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

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PROBLEM

Automated damage detection for disaster response using domain adaptation techniques. Within human-machine systems (HMS) for disaster management, automated damage detection provides decision-makers with actionable situational awareness.

METHOD

Full 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.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. Comprehensive ablation experiments on the unseen Ida-BD test split demonstrate that SDA is indispensable: removing it causes damage detection to fail entirely.

WHY NOW

Disaster Response moved forward this cycle; last verified April 2026. Public score 6.0/10.

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Opportunity summary

Score6.0

PainAutomated damage detection for disaster response using domain adaptation techniques.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Automated damage detection for disaster response using domain adaptation techniques.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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

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

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