Duplicate from huilihuang413/EIDSeg

Co-authored-by: Huili Huang <huilihuang413@users.noreply.huggingface.co>

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README.md

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+---
+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}
+}
+```