Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
climate
License:
| license: mit | |
| task_categories: | |
| - image-segmentation | |
| language: | |
| - en | |
| tags: | |
| - climate | |
| pretty_name: EIDSeg | |
| size_categories: | |
| - 1K<n<10K | |
| # EIDSeg: A Pixel-Level Semantic Segmentation Dataset for Post-Earthquake Damage Assessment from Social Media Images | |
| EIDSeg is a large-scale **post-earthquake infrastructure damage segmentation dataset** collected from nine major earthquakes (2008–2023). | |
| This repository provides the **raw dataset** in **CVAT XML format**, along with the corresponding images organized by split. | |
| It is intended to be used together with our official codebase for parsing XML annotations and training segmentation models. | |
| See our [github repo](https://github.com/HUILIHUANG413/EIDSeg) for more detail. | |
| ## 📥 Downloading the Dataset | |
| You can download the dataset using **any of the methods below**. | |
| ### 🔹 1. Using `huggingface_hub` | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| snapshot_download( | |
| repo_id="HuiliHuang/EIDSeg", | |
| repo_type="dataset", | |
| local_dir="EIDSeg" | |
| ) | |
| ``` | |
| ## 📁 Data Layout | |
| The code expects CVAT-style XML annotations and images arranged like: | |
| ``` | |
| data/ | |
| ├── train/ | |
| │ ├── train.xml | |
| │ └── images/ | |
| │ └── default/ | |
| │ ├── 0001.jpg | |
| │ ├── 0002.png | |
| │ └── ... | |
| └── val/ | |
| ├── val.xml | |
| └── images/ | |
| └── default/ | |
| ├── 1001.jpg | |
| └── ... | |
| ``` | |
| **Annotations** (CVAT XML): | |
| ```xml | |
| <annotations> | |
| <image name="0001.jpg" ...> | |
| <polygon label="D_Building" points="x1,y1;x2,y2;..." /> | |
| <polygon label="UD_Road" points="..." /> | |
| ... | |
| </image> | |
| </annotations> | |
| ``` | |
| **Class mapping (6 classes):** | |
| ``` | |
| 0: UD_Building | |
| 1: D_Building | |
| 2: Debris | |
| 3: UD_Road | |
| 4: D_Road | |
| 5: void (Background / Undesignated) | |
| ``` | |
| ## Benchmark Results | |
| Semantic Segmentation Benchmark of EIDSeg | |
| | Model | Backbone | Pre-train | Input | mIoU (%) | FWIoU (%) | PA (%) | FLOPs (G) | Params (M) | | |
| |:---------------:|:----------:|:----------:|:------:|:--------:|:---------:|:------:|:---------:|:----------:| | |
| | DeepLabV3+ | ResNet-101 | Cityscapes | 512² | 67.1 | 68.2 | 86.0 | 79.29 | 58.76 | | |
| | SegFormer | MiT-B5 | Cityscapes | 512² | 74.4 | 75.2 | 86.9 | 110.16 | 84.60 | | |
| | Mask2Former-S | Swin-S | Cityscapes | 512² | 76.1 | 77.1 | 87.7 | 93.21 | 81.42 | | |
| | Mask2Former-L | Swin-L | Cityscapes | 512² | 77.4 | 78.4 | 88.7 | 250.54 | 215.45 | | |
| | BEiT-B | ViT-B | ADE20K | 640² | 78.7 | 79.6 | 89.8 | 1823.53 | 441.09 | | |
| | BEiT-L | ViT-L | ADE20K | 640² | 79.0 | 79.8 | 89.9 | 3182.73 | 311.62 | | |
| | OneFormer | Swin-L | Cityscapes | 512² | 79.8 | 80.2 | 89.8 | 1042.14 | 218.77 | | |
| | **EoMT** | ViT-L | Cityscapes | 1024² | **80.8** | **80.9** | **90.3** | 1341.85 | 319.02 | | |
| Class-wise IoU and mIoU (%) for each model on EIDSeg | |
| | Model | UD_Building | D_Building | Debris | UD_Road | D_Road | mIoU (%) | | |
| |:--------------:|:-----------:|:-----------:|:------:|:-------:|:------:|:--------:| | |
| | DeepLabV3+ | 34.5 | 65.4 | 77.3 | 75.7 | 73.7 | 67.1 | | |
| | SegFormer | 54.9 | 73.5 | 82.3 | 79.9 | 79.4 | 74.4 | | |
| | Mask2Former-S | 58.9 | 76.7 | 83.8 | 80.2 | 80.1 | 76.1 | | |
| | Mask2Former-L | 63.5 | 76.9 | 84.9 | 82.0 | 80.9 | 77.4 | | |
| | BEiT-B | 66.0 | 76.7 | **85.1** | 82.3 | 78.7 | 78.7 | | |
| | BEiT-L | 66.4 | 77.9 | **85.1** | 82.6 | 78.7 | 79.0 | | |
| | OneFormer | 68.7 | 79.7 | 85.0 | **84.1** | 79.9 | 79.8 | | |
| | **EoMT** | **70.1** | **80.0** | 84.6 | 82.0 | **87.3** | **80.8** | | |
| ## Contact | |
| Huili Huang - huilihuang1997@gmail.com; hhuang413@gatech.edu | |
| Please ⭐ if you find it useful so that I find the motivation to keep improving this. Thanks | |
| ## Citation | |
| If you find this work or the EIDSeg dataset useful in your research, please consider citing our paper. Your citation helps support and encourage future development of this project. | |
| ``` | |
| @article{huang2025eidseg, | |
| title = {EIDSeg: Post-Earthquake Infrastructure Damage Segmentation Dataset}, | |
| author = {Huili Huang and Chengeng Liu and Danrong Zhang and Shail Patel and Anastasiya Masalava and Sagar Sadak and Parisa Babolhavaeji and Weihong Low and Max Mahdi Roozbahani and J.~David Frost}, | |
| journal = {arXiv preprint arXiv:https://arxiv.org/abs/2511.06456}, | |
| year = {2025} | |
| } | |
| ``` |