# Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response

arXiv: https://arxiv.org/abs/2511.03132 · HTML: https://mlsanity.com/paper/2511.03132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2202.12361/) (2022)
- [MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos](https://mlsanity.com/paper/2006.16479/) (2020)
- [Correcting rural building annotations in OpenStreetMap using convolutional neural networks](https://mlsanity.com/paper/1901.08190/) (2019)
- [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://mlsanity.com/paper/2407.17673/) (2024)
- [Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event](https://mlsanity.com/paper/2306.12589/) (2023)
- [Quantitative Data Analysis: CRASAR Small Unmanned Aerial Systems at Hurricane Ian](https://mlsanity.com/paper/2308.14577/) (2023)
- [Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters](https://mlsanity.com/paper/2405.06593/) (2024)
- [Wireless Network Demands of Data Products from Small Uncrewed Aerial Systems at Hurricane Ian](https://mlsanity.com/paper/2303.12937/) (2023)
- [Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery](https://mlsanity.com/paper/2505.08117/) (2025)
- [Challenges and Research Directions from the Operational Use of a Machine Learning Damage Assessment System via Small Uncrewed Aerial Systems at Hurricanes Debby and Helene](https://mlsanity.com/paper/2506.15890/) (2025)

## Built on by (most cited first, 1 of 1)

- [Instruct-ICL: Instruction-Guided In-Context Learning for Post-Disaster Damage Assessment](https://mlsanity.com/paper/2605.11439/) (2026)

## Abstract (arXiv)

This paper presents the first AI/ML system for automating building damage assessment in uncrewed aerial systems (sUAS) imagery to be deployed operationally during federally declared disasters (Hurricanes Debby and Helene). In response to major disasters, sUAS teams are dispatched to collect imagery of the affected areas to assess damage; however, at recent disasters, teams collectively delivered between 47GB and 369GB of imagery per day, representing more imagery than can reasonably be transmitted or interpreted by subject matter experts in the disaster scene, thus delaying response efforts. To alleviate this data avalanche encountered in practice, computer vision and machine learning techniques are necessary. While prior work has been deployed to automatically assess damage in satellite imagery, there is no current state of practice for sUAS-based damage assessment systems, as all known work has been confined to academic settings. This work establishes the state of practice via the development and deployment of models for building damage assessment with sUAS imagery. The model development involved training on the largest known dataset of post-disaster sUAS aerial imagery, containing 21,716 building damage labels, and the operational training of 91 disaster practitioners. The best performing model was deployed during the responses to Hurricanes Debby and Helene, where it assessed a combined 415 buildings in approximately 18 minutes. This work contributes documentation of the actual use of AI/ML for damage assessment during a disaster and lessons learned to the benefit of the AI/ML research and user communities.