Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts. However, performing such building damage assessments in an automated manner is non-trivial due to the challenges posed by variations in disaster-specific damage, diversity in satellite imagery, and the dearth of extensive, labeled datasets. To circumvent these issues, this paper introduces a human-in-the-loop workflow for rapidly training building damage assessment models after a natural disaster. This article details a case study using this workflow, executed in partnership with the American Red Cross during a tornado event in Rolling Fork, Mississippi in March, 2023. The output from our human-in-the-loop modeling process achieved a precision of 0.86 and recall of 0.80 for damaged buildings when compared to ground truth data collected post-disaster. This workflow was implemented end-to-end in under 2 hours per satellite imagery scene, highlighting its potential for real-time deployment.

翻译：在自然灾害发生后，利用高分辨率卫星影像对建筑损伤进行快速而准确的评估，对于指导并优化一线救援响应工作至关重要。然而，由于灾害特异性破坏模式、卫星影像多样性的挑战，以及大规模标注数据集的匮乏，实现自动化建筑损伤评估绝非易事。为解决这些问题，本文提出了一种"人机协同"工作流，旨在自然灾害发生后快速训练建筑损伤评估模型。本文详细介绍了运用该工作流开展的一项案例研究——与美国红十字会合作，针对2023年3月密西西比州罗灵福克市龙卷风事件展开评估。与灾后收集的地面实况数据相比，我们的人机协同建模过程对受损建筑的检测精确率达0.86，召回率达0.80。该工作流以每景卫星影像不超过2小时的速度实现端到端部署，凸显了其实时应用的潜力。