| --- |
| AIGC: |
| Label: '1' |
| ContentProducer: 001191440300708461136T1XGW3 |
| ProduceID: a7fb9d1898fb1b9393c25aa5549282e2_3ea031a5841011f18a64525400826444 |
| ReservedCode1: >- |
| 5uAV48gislVE7276GNz5GPBTLzSBBnoHC1Jznu0RpcIqAJ0R//63CTDj/sX1z9L59JEC44DCoY1PmdUgEq0KWg5mWr/nH0p0LrShKtQq07dsM/488tjMMD3IUuMIvbeiOOt9LdoPcu+m75KpByl5QFIXDG4HLc7ej0iqZxDw7DbkTHBHtJkllrmrjnE= |
| ContentPropagator: 001191440300708461136T1XGW3 |
| PropagateID: a7fb9d1898fb1b9393c25aa5549282e2_3ea031a5841011f18a64525400826444 |
| ReservedCode2: >- |
| 5uAV48gislVE7276GNz5GPBTLzSBBnoHC1Jznu0RpcIqAJ0R//63CTDj/sX1z9L59JEC44DCoY1PmdUgEq0KWg5mWr/nH0p0LrShKtQq07dsM/488tjMMD3IUuMIvbeiOOt9LdoPcu+m75KpByl5QFIXDG4HLc7ej0iqZxDw7DbkTHBHtJkllrmrjnE= |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-segmentation |
| language: |
| - en |
| tags: |
| - remote-sensing |
| - earthquake |
| - uav |
| - semantic-segmentation |
| - disaster-response |
| - computer-vision |
| pretty_name: EarthquakeNet |
| size_categories: |
| - 10B<n<100B |
| --- |
| |
| <div align="center"> |
| <h1>EarthquakeNet</h1> |
| |
| [ICIP 2024] Official repository for **EarthquakeNet: A High-Resolution UAV-Based Dataset for Earthquake Damage Assessment** |
| *Published in: 2024 IEEE International Conference on Image Processing (ICIP)* |
| |
| <a href="https://doi.org/10.1109/ICIP51287.2024.10648157" target="_blank" rel="noopener noreferrer"> |
| <img src="https://img.shields.io/badge/DOI-10.1109%2FICIP51287.2024.10648157-blue" alt="DOI"> |
| </a> |
| <a href="https://huggingface.co/datasets/YXBIAN/EarthquakeNet" target="_blank" rel="noopener noreferrer"> |
| <img src="https://img.shields.io/badge/Hugging%20Face-Dataset-yellow?logo=huggingface" alt="Hugging Face"> |
| </a> |
| <a href="https://drive.google.com/drive/folders/1RGmFLJla8ogRWQGVRjJ3VaQ4503g7Ha8" target="_blank" rel="noopener noreferrer"> |
| <img src="https://img.shields.io/badge/Google%20Drive-Sample%20Data-blue?logo=googledrive" alt="Google Drive"> |
| </a> |
| <a href="https://github.com/YuxinBian/EarthquakeNet/" target="_blank" rel="noopener noreferrer"> |
| <img src="https://img.shields.io/badge/GitHub-Repo-black?logo=github" alt="GitHub"> |
| </a> |
| <a href="LICENSE" target="_blank" rel="noopener noreferrer"> |
| <img src="https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-green.svg" alt="License"> |
| </a> |
| |
| [Shenlu Jiang](https://sites.google.com/view/shenlu92)<sup>a</sup>, [Yuxin Bian](https://orcid.org/0009-0000-9999-2895)<sup>b</sup>, [Yiran Wang](https://github.com/wang0298)<sup>b</sup>, Xufeng Li<sup>a</sup>, Zhankeng Liu<sup>a</sup>, Yi Ren<sup>a</sup>, Yunxuan Zhao<sup>a</sup> |
| <sup>a</sup> School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China. |
| <sup>b</sup> Faculty of Science, The University of Hong Kong, Pokfulam, Hong Kong. |
| </div> |
|
|
| <div align="center"> |
| <img src="./image/sample_annotations.png" width="95%" alt="EarthquakeNet sample annotations"> |
| <br> |
| <em></em> |
| </div> |
|
|
| --- |
|
|
| ## 📰 News |
|
|
| - [2024.9.30] The dataset is released on [Hugging Face](https://huggingface.co/datasets/YXBIAN/EarthquakeNet). |
| - [2024.9.27] Our paper is published in [IEEE Xplore](https://doi.org/10.1109/ICIP51287.2024.10648157). |
| - [2024.6.7] Our manuscript has been accepted for presentation at [IEEE ICIP 2024](https://cmsworkshops.com/ICIP2024/view_paper.php?PaperNum=1825). |
| --- |
|
|
| ## 📝 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). |
|
|
| <div align="center"> |
| <img src="./image/weather_diversity.png" width="95%" alt="Weather diversity in EarthquakeNet"> |
| <br> |
| <em></em> |
| </div> |
|
|
| --- |
|
|
| ## 🗂️ 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) | |
|
|
| --- |
|
|
| ### 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) | |
|
|
| --- |
|
|
| ## 📚 Citation |
|
|
| If you use EarthquakeNet in your research, please cite: |
|
|
| ```bibtex |
| @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}, |
| 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 disaster damage assessment.}, |
| keywords={Deep learning;Computer vision;Image resolution;Annotations;Disasters;Semantic segmentation;Roads;Post-earthquake assesment;Land use detection;Semantic segmentation}, |
| doi={10.1109/ICIP51287.2024.10648157}, |
| ISSN={2381-8549}, |
| month={Oct},} |
| ``` |
|
|
| --- |
|
|
| ## 📄 License |
|
|
| This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. |
|
|
| --- |
|
|
| ## 📧 Contact |
|
|
| For questions and collaboration, please reach out to [Yuxin Bian](https://orcid.org/0009-0000-9999-2895)(<yxbian@connect.hku.hk>). |
