Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event
This addresses the need for quick and accurate damage assessments to aid first responders, though it is incremental as it builds on existing human-in-the-loop and modeling approaches.
The paper tackled the problem of rapid building damage assessment after natural disasters by introducing a human-in-the-loop workflow, achieving a precision of 0.86 and recall of 0.80 for damaged buildings in a case study during the 2023 Rolling Fork tornado event, with implementation in under 2 hours per scene.
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.