{
 "id": "YXBIAN/EarthquakeNet",
 "owner": "YXBIAN",
 "name": "EarthquakeNet",
 "downloads": 71,
 "likes": 2,
 "last_modified": "2026-08-14T08:47:01.000Z",
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 "licence_link": null,
 "file_licence": "cc-by-nc-sa-4.0",
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 ],
 "sources": [],
 "gated": "auto",
 "tasks": [
  "image-segmentation"
 ],
 "citation": "@INPROCEEDINGS{10648157,\n  author={Jiang, Shenlu and Bian, Yuxin and Wang, Yiran and Li, Xufeng and Liu, Zhankeng and Ren, Yi and Zhao, Yunxuan},\n  booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, \n  title={EarthquakeNet: A High-Resolution UAV-Based Dataset for Earthquake Damage Assessment}, \n  year={2024},\n  volume={},\n  number={},\n  pages={55-61},\n  abstract={Advancements in computer vision and deep learning have significantly propelled progress in scene understanding, aiding rescue teams in accurately assessing damage after natural disasters. In this paper, we introduce EarthquakeNet, a meticulously curated high-resolution post-earthquake dataset featuring detailed classification and semantic segmentation annotations, designed to enhance comprehensive scene understanding following natural disasters. EarthquakeNet comprises post-disaster images captured using unmanned aerial vehicles (UAVs) from multiple affected areas after an earthquake. The uniqueness of EarthquakeNet lies in providing high-resolution post-disaster imagery, each with exhaustive annotations. Unlike existing datasets that offer annotations for specific scene elements like buildings, EarthquakeNet provides pixel-level annotations for a broader range of categories, including roads, houses, and tents. We also demonstrate the utility of the dataset by implementing state-of-the-art segmentation models on EarthquakeNet, showcasing its value in improving existing methods for natural disa",
 "signal": "restrictive",
 "reason": "The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.",
 "revision": "c53ad07440250e377a5ae60c2aea5ae8868fedb3",
 "stated_evidence": "https://huggingface.co/datasets/YXBIAN/EarthquakeNet/blob/c53ad07440250e377a5ae60c2aea5ae8868fedb3/README.md",
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}