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

The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: L2HNet. A more detailed description of the data can be found in the paper. 

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 questions, contact us at ashelee@whu.edu.cn (Zhuohong Li, 李卓鸿). 

• April 2026: We have updated a script to handle edge overlaps in SinoLC-1, addressing incorrect overlaps appearing on the right or bottom boundaries of certain cities. Please visit Code to view the script, or email Dr. Zhuohong Li to specify which cities need to be processed.
• March 2024: 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). 
• March 2026: We updated SinoLC-1 and released a building-level urban functional mapping product for China (SinoBF-1), which was accepted by Nature Communications (Paper link), and the dataset has been released (Data download).

• 2026年4月: 我们更新了一个处理SinoLC-1边缘重叠的脚本,以解决某些城市的边界右侧或下侧出现的不正确重叠,请访问Code 查看脚本,或给李卓鸿博士发邮件告知需处理的城市

• 2026年3月:我们对SinoLC-1进行了更新,推出了"中国楼栋尺度的建筑功能制图产品(SinoBF-1)",包含109个城市建筑功能(住宅、学校、医院、行政、工厂、文体等), 该产品被Nature Communications接收 (Paper link), 数据已经开源 (Data download)

• 2024年3月:我们的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。

More related work can be found on my homepage. (更多相关工作可在我的主页查看)

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.