LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation (NeurIPS2021 Benchmark and Dataset Track)

Overview

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zhong


This is an official implementation of LoveDA in our NeurIPS2021 paper " LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation"

Citation

If you use FactSeg in your research, please cite our coming NeurIPS2021 paper.

    @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={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
    year={2021},
    url={https://openreview.net/forum?id=bLBIbVaGDu}
    }

Dataset

Coming Soon!

Issues
  • Invitation of incoporating LoveDA dataset into MMSegmentation.

    Invitation of incoporating LoveDA dataset into MMSegmentation.

    Hi, I am member of OpenMMLab who develops MMSegmentation. Our vision is provide up-to-date methods and dataset(i.e., benchmark) for researchers and community around the world.

    First, congrats for acceptance of NeurIPS'21. I think this dataset and benchmark would definitely help Remote Sensing Image field where semantic segmentation plays an important role.

    Frankly speaking, right now we do not have too much human resources. Would you like to help us incpoorate your dataset into MMSegmentation? We appreciate all contibutors and users, here is our contributing details.

    I think if LoveDA is provided by MMSegmentation, it could let more people use & cite this excellent work, especially for those who want to establish standard segmentation benchmark.

    Looking forward to your reply. Wish you all the best.

    Best,

    opened by MengzhangLI 1
Owner
Kingdrone
Deep learning in RS
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