Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-555-2023
© 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-15-555-2023
© 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
| 
03 Feb 2023
Data description paper |
| 03 Feb 2023
| 03 Feb 2023
UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework
Qian Shi
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
Andrea Marinoni
Department of Physics and Technology, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
Xiaoping Liu
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
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Latest update: 13 Dec 2024
Short summary
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in...
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