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

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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. 
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Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable...
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