C3Net-for-building-extraction

This is a PyTorch implementation for our paper "Context-content collaborative network for building extraction from high-resolution imagery" has been published on Knowledge-Based System by Maoguo Gong, Tongfei Liu, Mingyang Zhang, Qingfu Zhang, Di Lu, Hanhong Zheng, Fenling Jiang.

Requirements

python
pytorch
opencv-python=4.1.0.25
scikit-image
scikit-learn
tqdm

Usage

Train

Load the train and test(val) data path
run: python train.py

Test

1. Load the model(pth)
2. Load the test data path
   run: python test.py

Get results (Visual and Quantitative)

Visual result: ./data/EastAsia/test/results
Quantitative result: ./test_acc.txt

Citation

If you find our work useful for your research, please consider citing our paper:

@article{gong2023context,
  title={Context-content collaborative network for building extraction from high-resolution imagery},
  author={Gong, Maoguo and Liu, Tongfei and Zhang, Mingyang and Zhang, Qingfu and Lu, Di and Zheng, Hanhong and Jiang, Fenlong},
  journal={Knowledge-Based Systems},
  pages={110283},
  year={2023},
  publisher={Elsevier}
}

Contact us

If you have any problme when running the code, please do not hesitate to contact us. Thanks.
E-mail: [email protected]
Date: March 5, 2023