SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (South of China)

The Version South of China includes the SinoLC-1 land-cover product for the provincial administrative regions of Guangdong, Guangxi, and Hainan.

Click to check all the data versions and download the data (点击查看/下载所有数据版本)

NOTE: If you have any data needs, questions, or technical issues, contact us at ashelee@whu.edu.cn (Zhuohong Li, 李卓鸿).

The land-cover mapping method with Python code is open-access at Code link. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (Paper link).

我们的最新制图算法被计算机视觉顶会CVPR2024接收(Paper link),代码开源在:Code link,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。

Citation format of the paper:
Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023. 

Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, ISPRS Journal of Photogrammetry and Remote Sensing. 192, pp.244-267, 2022.

BibTex format of the paper:

@article{li2023sinolc,
  title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data},
  author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan},
  journal={Earth System Science Data},
  volume={15},
  number={11},
  pages={4749--4780},
  year={2023},
  publisher={Copernicus Publications G{\"o}ttingen, Germany}
}

@article{li2022breaking,
  title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels},
  author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={192},
  pages={244--267},
  year={2022},
  publisher={Elsevier}
}