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.Comment: In submission to the 2023 ICCV Humanitarian Assistance and Disaster
Response Worksho