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https://github.com/fzmi/ubdd
[ICDM 2023] Code implementation of "Learning Efficient Unsupervised Satellite Image-based Building Damage Detection"
https://github.com/fzmi/ubdd
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[ICDM 2023] Code implementation of "Learning Efficient Unsupervised Satellite Image-based Building Damage Detection"
- Host: GitHub
- URL: https://github.com/fzmi/ubdd
- Owner: fzmi
- License: mit
- Created: 2023-12-04T00:27:58.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-12T12:04:01.000Z (10 months ago)
- Last Synced: 2024-05-23T00:04:31.497Z (8 months ago)
- Language: Python
- Homepage:
- Size: 5.33 MB
- Stars: 16
- Watchers: 3
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# U-BDD++: Unsupervised Building Damage Detection from Satellite Imagery
Code implementation of "Learning Efficient Unsupervised Satellite Image-based Building Damage Detection" from ICDM 2023.
[[Paper on ArXiv (Full Ver.)](https://arxiv.org/abs/2312.01576)] [[Paper on ICDM (Short Ver.)](https://ieeexplore.ieee.org/abstract/document/10415707)] [[BibTeX](#citation)]
## Overview
This repository contains code for U-BDD++.
## U-BDD Benchmark
### Data Preparation
Our work uses the public xBD dataset from xView2 challenge. You can find the dataset from [here](https://xview2.org/dataset) (account required). Please download the "Challenge training set", "Challenge test set" and "Challenge holdout set" datasets and follow the instructions on the website to unpack the files.
After downloading the dataset, the file structure should be similar to:
```
[xBD root folder]
├── hold
│ ├── images
│ └── labels
├── test
│ ├── images
│ └── labels
└── train
├── images
└── labels
```
Firstly, the data needs to be preprocessed before training. Please run the following command to preprocess the data:
```sh
python datasets/preprocess-data.py --data_dir
```
This will create a new folder `masks` under each dataset split folder, which contains the damage masks for each building.
## Installation
To start, please clone this repository to your local machine and follow the instructions below.
### Requirements
This repository requires `python>=3.9`, `torch>=1.13` and `torchvision>=0.14`. Older versions may work, but they are not tested.
> [!NOTE]
> As per installation requirement from Grounding DINO, please make sure the environment variable `CUDA_HOME` is set.
>
> `export CUDA_HOME=/path/to/cuda-xx.x`
```sh
# Grounding DINO does not support CUDA 12+ yet, this sample index-url uses CUDA 11.8
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
pip install git+https://github.com/IDEA-Research/GroundingDINO.git
pip install git+https://github.com/facebookresearch/segment-anything.git
pip install git+https://github.com/openai/CLIP.git
pip install -r requirements.txt
```
> [!NOTE]
> Additionally, DINO requires building the custom PyTorch ops for MultiScaleDeformableAttention:
> ```sh
> cd models/dino/models/dino/ops
> python setup.py build install
> ```
### Pre-trained Weights
You can download the pre-trained weights of U-BDD++ for evaluation.
| Model | Backbone | Weights |
| :---: | :---: | :---: |
| U-BDD++ | Resnet | [Google Drive](https://drive.google.com/file/d/1UQGCtadrJ2RFZZDrI43wJ0Ed1MJRiDhK/view?usp=sharing) |
| U-BDD++ | Swin | [Google Drive](https://drive.google.com/file/d/178OV_VW2BzBZzJOK5ajimo_PykkxH6Ui/view?usp=sharing) |
[Coming Soon]
## Evaluation
To evaluate U-BDD++ on xBD dataset, please run:
```sh
CUDA_VISIBLE_DEVICES=0 python predict-pretrain.py --test-set-path "path/to/xbd/test" --dino-path "path/to/dino/weights" --dino-config "path/to/dino/config" --sam-path "path/to/sam/weights"
# for example
CUDA_VISIBLE_DEVICES=0 python predict-pretrain.py --test-set-path "/home/datasets/xbd/test" --dino-path "/home/ubdd-dino-resnet.pth" --dino-config "/home/ubdd/models/dino/config/DINO_4scale_UBDD_resnet.py" --sam-path "/home/checkpoints/SAM/sam_vit_h_4b8939.pth"
```
## License
This repository is released under the MIT license. Please see the [LICENSE](LICENSE) file for more information.
## Attribution
Part of this repository used the following repositories:
Related repositories:
- [DINO](https://github.com/IDEA-Research/DINO)
- [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)
- [Segment Anything](https://github.com/facebookresearch/segment-anything)
- [CLIP](https://github.com/openai/CLIP)
- [BDANet](https://github.com/ShaneShen/BDANet-Building-Damage-Assessment)
Thanks to the authors for their great work!
## Citation
If you find this repository useful in your research, please use the following BibTeX for citation:
```bibtex
@article{zhang2023learning,
title={Learning Efficient Unsupervised Satellite Image-based Building Damage Detection},
author={Zhang, Yiyun and Wang, Zijian and Luo, Yadan and Yu, Xin and Huang, Zi},
journal={arXiv preprint arXiv:2312.01576},
year={2023}
}
```