mlsanityData to 14 Oct 2026

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

arXiv 2202.12361 ↗First posted 24 Feb 2022cs.CVcited by 40

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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.

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

20 of 30 references.

References · 20

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  3. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  4. 2016Pyramid Scene Parsing Networkcited by 2,479
  5. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  6. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  7. 2016COCO-Stuff: Thing and Stuff Classes in Contextcited by 864
  8. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  9. 2017Functional Map of the Worldcited by 338
  10. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 277
  11. 2020FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understandingcited by 181
  12. 2019xBD: A Dataset for Assessing Building Damage from Satellite Imagerycited by 139
8 more
  1. 2019Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehiclescited by 25
  2. 2020MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videoscited by 25
  3. 2020Detecting natural disasters, damage, and incidents in the wildcited by 23
  4. 2018Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagerycited by 22
  5. 2020Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessmentcited by 12
  6. 2018Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagerycited by 10
  7. 2018From Satellite Imagery to Disaster Insightscited by 9
  8. 2017Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crisescited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 16; 2026: 17.

40 papers.

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  1. 2025BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster responsecited by 41
  2. 2025RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Eventscited by 14
  3. 2024RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agentscited by 11
  4. 2026Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operationscited by 6
  5. 2024UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Taskscited by 4
  6. 2025ZeShot-VQA: Zero-Shot Visual Question Answering Framework with Answer Mapping for Natural Disaster Damage Assessmentcited by 4
  7. 2026Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Taskscited by 4
  8. 20253DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessmentcited by 3
  9. 2024UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Trackingcited by 2
  10. 2025Post-Hurricane Debris Segmentation Using Fine-Tuned Foundational Vision Modelscited by 2
  11. 2025Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagerycited by 2
  12. 2024Exploring Text-Guided Single Image Editing for Remote Sensing Imagescited by 1
28 more
  1. 2024Multi-step feature fusion for natural disaster damage assessment on satellite imagescited by 1
  2. 2025LightFormer: A lightweight and efficient decoder for remote sensing image segmentationcited by 1
  3. 2025Light-Weight Cross-Modal Enhancement Method with Benchmark Construction for UAV-based Open-Vocabulary Object Detectioncited by 1
  4. 2025Assessing On-the-Ground Disaster Impact Using Online Data Sourcescited by 1
  5. 2025Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Responsecited by 1
  6. 2025Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessmentcited by 1
  7. 20253D Semantic Segmentation for Post-Disaster Assessmentcited by 1
  8. 2024TerrAInav Sim: An Open-Source Simulation of UAV Aerial Imaging from Satellite Datacited by 0
  9. 2024Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learningcited by 0
  10. 2025Multi-Label Classification Framework for Hurricane Damage Assessmentcited by 0
  11. 2025Structured AI Decision-Making in Disaster Managementcited by 0
  12. 2025CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentationcited by 0
  13. 2025A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagerycited by 0
  14. 2026Open-Vocabulary vs Supervised Learning Methods for Post-Disaster Visual Scene Understandingcited by 0
  15. 2026TornadoNet: Real-Time Building Damage Detection with Ordinal Supervisioncited by 0
  16. 2026Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applicationscited by 0
  17. 2026Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Modelscited by 0
  18. 2026Instruct-ICL: Instruction-Guided In-Context Learning for Post-Disaster Damage Assessmentcited by 0
  19. 2026OPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentationcited by 0
  20. 2026iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervisioncited by 0
  21. 2026GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligencecited by 0
  22. 2026Beyond Damage Assessment: Recyclable Material Detection in Aerial Disaster Imagery Using a Lightweight Patch-Based Frameworkcited by 0
  23. 2026JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answeringcited by 0
  24. 2026Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streamscited by 0
  25. 2026Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagerycited by 0
  26. 2026Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessmentcited by 0
  27. 2026Rapid Debris-Volume Estimation from Post-Hurricane Aerial Imagerycited by 0
  28. 2026Semantic Map Sharing and Capability-Aware Coverage Planning for AI-Native 6G Robotic Coordinationcited by 0