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Rapid and accurate building damage assessments from high-resolution satelliteimagery following a natural disaster is essential to inform and optimize firstresponder efforts. However, performing such building damage assessments in anautomated manner is non-trivial due to the challenges posed by variations indisaster-specific damage, diversity in satellite imagery, and the dearth ofextensive, labeled datasets. To circumvent these issues, this paper introducesa human-in-the-loop workflow for rapidly training building damage assessmentmodels after a natural disaster. This article details a case study using thisworkflow, executed in partnership with the American Red Cross during a tornadoevent in Rolling Fork, Mississippi in March, 2023. The output from ourhuman-in-the-loop modeling process achieved a precision of 0.86 and recall of0.80 for damaged buildings when compared to ground truth data collectedpost-disaster. This workflow was implemented end-to-end in under 2 hours persatellite imagery scene, highlighting its potential for real-time deployment.

Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event