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

arXiv: https://arxiv.org/abs/2306.12589 · HTML: https://mlsanity.com/paper/2306.12589/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 21 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery](https://mlsanity.com/paper/2004.07312/) (2020)
- [Assessing out-of-domain generalization for robust building damage detection](https://mlsanity.com/paper/2011.10328/) (2020)
- [Fully convolutional Siamese neural networks for buildings damage assessment from satellite images](https://mlsanity.com/paper/2111.00508/) (2021)
- [xFBD: Focused Building Damage Dataset and Analysis](https://mlsanity.com/paper/2212.13876/) (2022)

## Built on by (most cited first, 8 of 8)

- [GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots](https://mlsanity.com/paper/2404.15500/) (2024)
- [Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal](https://mlsanity.com/paper/2508.00858/) (2025)
- [Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters](https://mlsanity.com/paper/2405.06593/) (2024)
- [Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery](https://mlsanity.com/paper/2503.14860/) (2025)
- [Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery](https://mlsanity.com/paper/2505.08117/) (2025)
- [Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response](https://mlsanity.com/paper/2511.03132/) (2025)
- [Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery](https://mlsanity.com/paper/2403.02736/) (2024)
- [HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment](https://mlsanity.com/paper/2607.11838/) (2026)

## Abstract (arXiv)

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.