EarthquakeNet: A High-Resolution UAV-Based Dataset for Earthquake Damage Assessment

Shenlu Jiang, Yuxin Bian, Yiran Wang, Xufeng Li, Zhankeng Liu, Yi Ren, Yunxuan Zhao

Published in: 2024 IEEE International Conference on Image Processing (ICIP)

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Overview

EarthquakeNet is a high-resolution UAV semantic segmentation dataset for post-earthquake damage assessment. It was collected using a fixed-wing UAV after the 2013 Lushan Earthquake in Baoxing County, Sichuan Province, China.

The dataset is distributed as EarthquakeNet_v1.0.zip. After extraction, it contains training and validation images with pixel-level semantic segmentation annotations for 9 classes (8 foreground + background).

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Dataset Statistics

Property	Value
Original UAV images	69
Weather conditions	45 cloud-free / 14 light cloud / 10 heavy cloud
Original resolution	5616 × 3744
Annotation type	Pixel-level semantic segmentation
Number of classes	9 (8 foreground + background)

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Semantic Classes

ID	Class	RGB
0	Background	(0,0,0)
1	Building – No damage	(0,255,0)
2	Building – Slight/moderate damage	(255,255,0)
3	Building – Bad/heavy damage	(255,120,0)
4	Building – Collapsed	(255,0,0)
5	Road – No damage	(150,150,150)
6	Road – Slight/moderate damage	(150,150,255)
7	Road – Heavy damage	(0,150,255)
8	Tent	(255,0,255)

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Citation

If you use EarthquakeNet in your research, please cite:

@INPROCEEDINGS{10648157,
  author={Jiang, Shenlu and Bian, Yuxin and Wang, Yiran and Li, Xufeng and Liu, Zhankeng and Ren, Yi and Zhao, Yunxuan},
  booktitle={2024 IEEE International Conference on Image Processing (ICIP)},
  title={EarthquakeNet: A High-Resolution UAV-Based Dataset for Earthquake Damage Assessment},
  year={2024},
  volume={},
  number={},
  pages={55-61},
  doi={10.1109/ICIP51287.2024.10648157}
}

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License

This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.