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Adaptive Sampling for Automated Post-Disaster Rapid Damage Assessment via Level-Set Cost-Aware Bayesian Optimization

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Computer Science > Machine Learning

arXiv:2608.02868 (cs)
[Submitted on 3 Aug 2026]

Title:Adaptive Sampling for Automated Post-Disaster Rapid Damage Assessment via Level-Set Cost-Aware Bayesian Optimization

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Abstract:Natural disasters frequently inflict severe damage to the built environment, which demands a rapid, reliable, and cost-effective damage assessment for emergency response. However, traditional methods for post-disaster damage assessment often rely on static, labor-intensive data collection strategies that can be prohibitively expensive and struggle to adapt to dynamic post-disaster conditions. In this study, we propose a cost-aware Bayesian optimization framework combined with level-set estimation that continuously guides autonomous data collectors, e.g., an unmanned aerial vehicle (UAV), toward the most informative regions. By dynamically updating damage estimates across different geographic zones, our approach systematically reduces uncertainty while minimizing operational costs. The proposed framework is first validated using a controlled synthetic toy study, demonstrating the agent's ability to efficiently trace damage boundaries, recover the underlying damage map, and rapidly reduce predictive uncertainty. Furthermore, the approach is evaluated using high-fidelity disaster data generated by the Regional Resilience Determination (R2D) software. The results of the algorithm provide accurate and timely damage estimates that support informative and fast emergency response.
Comments: 11 pages, 7 figures, 1 table. Accepted at the SIAM International Conference on Data Mining (SDM) 2026
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2608.02868 [cs.LG]
  (or arXiv:2608.02868v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.02868
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Boyang Xu [view email]
[v1] Mon, 3 Aug 2026 20:38:45 UTC (5,061 KB)
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