Awesome

[Paper], [Dataset], [BibTeX]

This is an official implementation of LoveNAS.

Environments:

• pytorch >= 1.11.0
• python >=3.6

pip install --upgrade git+https://github.com/Z-Zheng/ever.git
pip install git+https://github.com/qubvel/segmentation_models.pytorch
pip install mmcv-full==1.4.7 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html

The Swin-Transformer pretrained weights can be prepared following MMSegmentation.

Search architecture

bash ./scripts/nas_loveda.sh

Train model

The searched architectures and transferred encoder weights should be downloaded.

bash ./scripts/train_loveda.sh

Predict test results

The searched architectures and LoveNAS model weights should be downloaded. Submit the test results to LoveDA Semantic Segmentation Challenge to get scores.

bash ./scripts/submit_loveda.sh

LoveDA

The LoveDA dataset can be downloaded here.

Submit the test results to LoveDA Semantic Segmentation Challenge to get scores.

FloodNet

The FloodNet dataset can be downloaded here.

The train data should be prepared using prepare_floodnet.py.

Citation

If you use LoveNAS in your research, please cite the following papers.

    @article{wang2024lovenas,
      title={LoveNAS: Towards multi-scene land-cover mapping via hierarchical searching adaptive network},
      author={Wang, Junjue and Zhong, Yanfei and Ma, Ailong and Zheng, Zhuo and Wan, Yuting and Zhang, Liangpei},
      journal={ISPRS Journal of Photogrammetry and Remote Sensing},
      volume={209},
      pages={265--278},
      year={2024},
      publisher={Elsevier}
    }
    @inproceedings{wang2021loveda,
        title={Love{DA}: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation},
        author={Junjue Wang and Zhuo Zheng and Ailong Ma and Xiaoyan Lu and Yanfei Zhong},
        booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
        editor = {J. Vanschoren and S. Yeung},
        year={2021},
        volume = {1},
        pages = {},
        url={https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/4e732ced3463d06de0ca9a15b6153677-Paper-round2.pdf}
    }
    @article{wang2020rsnet,
      title={RSNet: The search for remote sensing deep neural networks in recognition tasks},
      author={Wang, Junjue and Zhong, Yanfei and Zheng, Zhuo and Ma, Ailong and Zhang, Liangpei},
      journal={IEEE Transactions on Geoscience and Remote Sensing},
      volume={59},
      number={3},
      pages={2520--2534},
      year={2020},
      publisher={IEEE}
    }