Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-4749-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
30 Oct 2023
Data description paper |
| 30 Oct 2023
| 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
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, PR China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, PR China
Mofan Cheng
School of Electronic Information, Wuhan University, Wuhan, 430079, PR China
Jingxin Hu
School of Electronic Information, Wuhan University, Wuhan, 430079, PR China
Guangyi Yang
School of Electronic Information, Wuhan University, Wuhan, 430079, PR China
Hongyan Zhang
CORRESPONDING AUTHOR
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, PR China
School of Computer Science, China University of Geosciences, Wuhan, 430074, PR China
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17 citations as recorded by crossref.
- Dynamic impact assessment of urban floods on the compound spatial network of buildings-roads-emergency service facilities Y. Zang et al. 10.1016/j.scitotenv.2024.172007
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- CCropLand30: High-resolution hybrid cropland maps of China created through the synergy of state-of-the-art remote sensing products and the latest national land survey L. Zhang et al. 10.1016/j.compag.2024.108672
- Coupling of microbial-explicit model and machine learning improves the prediction and turnover process simulation of soil organic carbon X. Xu et al. 10.1016/j.csag.2024.100001
- Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network Z. Xu & S. Zhao 10.1038/s41597-023-02844-2
- Simultaneous extraction of spatial and attributional building information across large-scale urban landscapes from high-resolution satellite imagery Z. Qian et al. 10.1016/j.scs.2024.105393
- CRformer: Multi-modal data fusion to reconstruct cloud-free optical imagery Y. Xia et al. 10.1016/j.jag.2024.103793
- Providing Quantitative and Site-Specific Decision Support for Urban Flooding Mitigation Using Machine Learning and SHAP E. Ke 10.2139/ssrn.4756021
- WenSiM: A Relative Accuracy Assessment Method for Land Cover Products Based on Optimal Transportation Theory R. Zhu et al. 10.3390/rs16020257
- Evaluation of six global high-resolution global land cover products over China Y. Wang et al. 10.1080/17538947.2023.2301673
- Storm surge risk assessment and sensitivity analysis based on multiple criteria decision-making methods: a case study of Huizhou City W. Wei et al. 10.3389/fmars.2024.1364929
- Local knowledge of homegarden plants in Miao ethnic communities in Laershan region, Xiangxi area, China J. Luo et al. 10.1186/s13002-024-00676-x
- Evaluation of Coupling Coordination Degree between Economy and Eco-Environment Systems in the Yangtze River Delta from 2000 to 2020 J. Ji et al. 10.3390/systems11100500
- SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data Z. Li et al. 10.5194/essd-15-4749-2023
- Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network Y. Jia et al. 10.3390/rs15174193
- Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series H. Jin et al. 10.3390/rs15204986
- Which land cover product provides the most accurate land use land cover map of the Yellow River Basin? W. Zhang et al. 10.3389/fevo.2023.1275054
9 citations as recorded by crossref.
- Dynamic impact assessment of urban floods on the compound spatial network of buildings-roads-emergency service facilities Y. Zang et al. 10.1016/j.scitotenv.2024.172007
- UANet: An Uncertainty-Aware Network for Building Extraction From Remote Sensing Images J. Li et al. 10.1109/TGRS.2024.3361211
- CCropLand30: High-resolution hybrid cropland maps of China created through the synergy of state-of-the-art remote sensing products and the latest national land survey L. Zhang et al. 10.1016/j.compag.2024.108672
- Coupling of microbial-explicit model and machine learning improves the prediction and turnover process simulation of soil organic carbon X. Xu et al. 10.1016/j.csag.2024.100001
- Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network Z. Xu & S. Zhao 10.1038/s41597-023-02844-2
- Simultaneous extraction of spatial and attributional building information across large-scale urban landscapes from high-resolution satellite imagery Z. Qian et al. 10.1016/j.scs.2024.105393
- CRformer: Multi-modal data fusion to reconstruct cloud-free optical imagery Y. Xia et al. 10.1016/j.jag.2024.103793
- Providing Quantitative and Site-Specific Decision Support for Urban Flooding Mitigation Using Machine Learning and SHAP E. Ke 10.2139/ssrn.4756021
- WenSiM: A Relative Accuracy Assessment Method for Land Cover Products Based on Optimal Transportation Theory R. Zhu et al. 10.3390/rs16020257
8 citations as recorded by crossref.
- Evaluation of six global high-resolution global land cover products over China Y. Wang et al. 10.1080/17538947.2023.2301673
- Storm surge risk assessment and sensitivity analysis based on multiple criteria decision-making methods: a case study of Huizhou City W. Wei et al. 10.3389/fmars.2024.1364929
- Local knowledge of homegarden plants in Miao ethnic communities in Laershan region, Xiangxi area, China J. Luo et al. 10.1186/s13002-024-00676-x
- Evaluation of Coupling Coordination Degree between Economy and Eco-Environment Systems in the Yangtze River Delta from 2000 to 2020 J. Ji et al. 10.3390/systems11100500
- SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data Z. Li et al. 10.5194/essd-15-4749-2023
- Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network Y. Jia et al. 10.3390/rs15174193
- Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series H. Jin et al. 10.3390/rs15204986
- Which land cover product provides the most accurate land use land cover map of the Yellow River Basin? W. Zhang et al. 10.3389/fevo.2023.1275054
Latest update: 09 May 2024
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.
Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable...
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