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

arXiv: https://arxiv.org/abs/2608.02868 · HTML: https://mlsanity.com/paper/2608.02868/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Scalable Variational Gaussian Process Classification](https://mlsanity.com/paper/1411.2005/) (2014)
- [Stochastic Variational Deep Kernel Learning](https://mlsanity.com/paper/1611.00336/) (2016)
- [Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images](https://mlsanity.com/paper/2208.02205/) (2022)
- [Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping](https://mlsanity.com/paper/2009.06200/) (2020)
- [An Intuitive Tutorial to Gaussian Process Regression](https://mlsanity.com/paper/2009.10862/) (2020)
- [Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery](https://mlsanity.com/paper/2404.19043/) (2024)
- [On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions](https://mlsanity.com/paper/2402.03006/) (2024)
- [UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations](https://mlsanity.com/paper/2202.10338/) (2022)

## Abstract (arXiv)

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.