# RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment

arXiv: https://arxiv.org/abs/2202.12361 · HTML: https://mlsanity.com/paper/2202.12361/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 24 Feb 2022.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [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)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Functional Map of the World](https://mlsanity.com/paper/1711.07846/) (2017)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [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)
- [Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles](https://mlsanity.com/paper/1906.08716/) (2019)
- [MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos](https://mlsanity.com/paper/2006.16479/) (2020)
- [Detecting natural disasters, damage, and incidents in the wild](https://mlsanity.com/paper/2008.09188/) (2020)
- [Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery](https://mlsanity.com/paper/1812.01756/) (2018)
- [Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2009.01193/) (2020)
- [Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery](https://mlsanity.com/paper/1812.05581/) (2018)
- [From Satellite Imagery to Disaster Insights](https://mlsanity.com/paper/1812.07033/) (2018)
- [Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises](https://mlsanity.com/paper/1704.02602/) (2017)

## Built on by (most cited first, 30 of 40)

- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events](https://mlsanity.com/paper/2509.01907/) (2025)
- [RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agents](https://mlsanity.com/paper/2410.13384/) (2024)
- [Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations](https://mlsanity.com/paper/2605.11633/) (2026)
- [UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Tasks](https://mlsanity.com/paper/2406.06230/) (2024)
- [ZeShot-VQA: Zero-Shot Visual Question Answering Framework with Answer Mapping for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2506.00238/) (2025)
- [Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks](https://mlsanity.com/paper/2606.10819/) (2026)
- [3DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessment](https://mlsanity.com/paper/2509.11097/) (2025)
- [UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking](https://mlsanity.com/paper/2409.03245/) (2024)
- [Post-Hurricane Debris Segmentation Using Fine-Tuned Foundational Vision Models](https://mlsanity.com/paper/2504.12542/) (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)
- [Exploring Text-Guided Single Image Editing for Remote Sensing Images](https://mlsanity.com/paper/2405.05769/) (2024)
- [Multi-step feature fusion for natural disaster damage assessment on satellite images](https://mlsanity.com/paper/2410.21901/) (2024)
- [LightFormer: A lightweight and efficient decoder for remote sensing image segmentation](https://mlsanity.com/paper/2504.10834/) (2025)
- [Light-Weight Cross-Modal Enhancement Method with Benchmark Construction for UAV-based Open-Vocabulary Object Detection](https://mlsanity.com/paper/2509.06011/) (2025)
- [Assessing On-the-Ground Disaster Impact Using Online Data Sources](https://mlsanity.com/paper/2509.11634/) (2025)
- [Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response](https://mlsanity.com/paper/2511.03132/) (2025)
- [Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessment](https://mlsanity.com/paper/2511.19557/) (2025)
- [3D Semantic Segmentation for Post-Disaster Assessment](https://mlsanity.com/paper/2512.24593/) (2025)
- [TerrAInav Sim: An Open-Source Simulation of UAV Aerial Imaging from Satellite Data](https://mlsanity.com/paper/2410.02003/) (2024)
- [Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning](https://mlsanity.com/paper/2412.10756/) (2024)
- [Multi-Label Classification Framework for Hurricane Damage Assessment](https://mlsanity.com/paper/2507.02265/) (2025)
- [Structured AI Decision-Making in Disaster Management](https://mlsanity.com/paper/2509.01576/) (2025)
- [CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2511.20302/) (2025)
- [A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagery](https://mlsanity.com/paper/2512.12128/) (2025)
- [Open-Vocabulary vs Supervised Learning Methods for Post-Disaster Visual Scene Understanding](https://mlsanity.com/paper/2603.01324/) (2026)
- [TornadoNet: Real-Time Building Damage Detection with Ordinal Supervision](https://mlsanity.com/paper/2603.11557/) (2026)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)
- [Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Models](https://mlsanity.com/paper/2605.08521/) (2026)
- [Instruct-ICL: Instruction-Guided In-Context Learning for Post-Disaster Damage Assessment](https://mlsanity.com/paper/2605.11439/) (2026)

## Abstract (arXiv)

Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment. In this paper, we present RescueNet, a meticulously curated high-resolution post-disaster dataset that includes detailed classification and semantic segmentation annotations. This dataset aims to facilitate comprehensive scene understanding in the aftermath of natural disasters. RescueNet comprises post-disaster images collected after Hurricane Michael, obtained using Unmanned Aerial Vehicles (UAVs) from multiple impacted regions. The uniqueness of RescueNet lies in its provision of high-resolution post-disaster imagery, accompanied by comprehensive annotations for each image. Unlike existing datasets that offer annotations limited to specific scene elements such as buildings, RescueNet provides pixel-level annotations for all classes, including buildings, roads, pools, trees, and more. Furthermore, we evaluate the utility of the dataset by implementing state-of-the-art segmentation models on RescueNet, demonstrating its value in enhancing existing methodologies for natural disaster damage assessment.