This directory curates papers on AI-enabled disaster assessment, response, and recovery. The intended scope includes building damage assessment, debris estimation, uncertainty quantification, crowdsourcing and human-AI teaming, multimodal disaster datasets, and rapid post-disaster mapping.
此目录收录 AI 赋能的灾害评估、响应与恢复论文。重点覆盖建筑损毁评估、灾害碎片估计、不确定性量化、众包与人机协同、多模态灾害数据集与快速灾后制图。
- Addresses a concrete disaster-cycle problem (preparedness, response, recovery, or mitigation) with stated data and sensing assumptions.
- Treats reliability as a first-class concern: uncertainty, human verification, or decision-relevant evaluation beyond a single accuracy score.
- Connects to the repository's long-term agenda of reliable spatial intelligence under incomplete and changing observations.
A post-hurricane building debris estimation workflow enabled by uncertainty-aware AI and crowdsourcing (2024)
- Authors: Chih-Shen Cheng, Amir Behzadan, Arash Noshadravan
- Venue: International Journal of Disaster Risk Reduction, Vol. 112, 104785
- Paper: https://doi.org/10.1016/j.ijdrr.2024.104785
- Access note: Closed access (Elsevier); no open-access PDF is legally redistributable, so this entry links to the DOI instead of bundling the file. / 该文为闭源订阅论文，无可合法转载的 PDF，故仅提供 DOI 链接。
- Core contribution: A human-AI teaming workflow that estimates post-hurricane building debris volume and composition from aerial imagery, combining uncertainty-aware AI detection and FEMA-based damage classification with a crowdsourcing module that reduces predictive uncertainty (case study: Hurricane Laura).
- Why it matters here: Directly relevant to debris-volume estimation pipelines; demonstrates how crowdsourced verification can shrink model uncertainty by up to ~40%, a template for uncertainty-aware post-disaster assessment.
- Limitations or open question: Single-event case study; transferability across hurricanes and regions, and integration with conformal or calibrated uncertainty, remain open.
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- **Limitations or open question:**See the English README or 中文 README for the current core reading path.