Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4749-2023
https://doi.org/10.5194/essd-15-4749-2023
Data description paper
 | 
30 Oct 2023
Data description paper |  | 30 Oct 2023

SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data

Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang

Related authors

CN_Wheat10: A 10 m resolution dataset of spring and winter wheat distribution in China (2018–2024) derived from time-series remote sensing
Man Liu, Wei He, and Hongyan Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-326,https://doi.org/10.5194/essd-2025-326, 2025
Preprint under review for ESSD
Short summary

Related subject area

Domain: ESSD – Land | Subject: Land Cover and Land Use
The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas
Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, and Marielle Saunois
Earth Syst. Sci. Data, 17, 2985–3008, https://doi.org/10.5194/essd-17-2985-2025,https://doi.org/10.5194/essd-17-2985-2025, 2025
Short summary
An annual 30 m cultivated-pasture dataset of the Tibetan Plateau from 1988 to 2021
Binghong Han, Jian Bi, Shengli Tao, Tong Yang, Yongli Tang, Mengshuai Ge, Hao Wang, Zhenong Jin, Jinwei Dong, Zhibiao Nan, and Jin-Sheng He
Earth Syst. Sci. Data, 17, 2933–2952, https://doi.org/10.5194/essd-17-2933-2025,https://doi.org/10.5194/essd-17-2933-2025, 2025
Short summary
GloUCP: a global 1 km spatially continuous urban canopy parameters for the WRF model
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data, 17, 2535–2551, https://doi.org/10.5194/essd-17-2535-2025,https://doi.org/10.5194/essd-17-2535-2025, 2025
Short summary
CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 17, 2193–2216, https://doi.org/10.5194/essd-17-2193-2025,https://doi.org/10.5194/essd-17-2193-2025, 2025
Short summary
U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025,https://doi.org/10.5194/essd-17-2147-2025, 2025
Short summary

Cited articles

Audebert, N., Le Saux, B., and Lefèvre, S.: Joint learning from earth observation and openstreetmap data to get faster better semantic maps, In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 21–26 July 2017, Honolulu, HI, USA, 67–75, https://doi.org/10.1109/CVPRW.2017.199, 2017. 
Bartholomé, E. and Belward, A. S.: GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, https://doi.org/10.1080/01431160412331291297, 2007. 
Boguszewski, A., Batorski, D., Ziemba-Jankowska, N., Dziedzic, T., and Zambrzycka, A.: LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery, In Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 19–20 June 2022, New Orleans, LA, USA, 1102–1110, https://doi.org/10.1109/cvprw53098.2021.00121, 2020. 
Cao, Y., and Huang, X.: A coarse-to-fine weakly supervised learning method for green plastic cover segmentation using high-resolution remote sensing images, ISPRS J. Photogramm. Remote Sens., 188, 157–176, https://doi.org/10.1016/j.isprsjprs.2022.04.012, 2022. 
Chang, G. H. and Brada, J. C.: The paradox of China's growing under-urbanization, Econ. Syst., 30, 24–40, https://doi.org/10.1016/j.ecosys.2005.07.002, 2006. 
Download
Short summary
Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Share
Altmetrics
Final-revised paper
Preprint