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

TL;DR

This paper presents a rapid, human-in-the-loop workflow for building damage assessment from satellite imagery, demonstrated during a 2023 tornado event, achieving high accuracy and real-time processing capabilities.

Contribution

The paper introduces a novel human-in-the-loop approach for quick training of damage assessment models, enabling real-time analysis post-disaster.

Findings

Precision of 0.86 for damaged buildings

Recall of 0.80 for damaged buildings

Workflow completed in under 2 hours per scene

Abstract

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.