UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework

Shi, Qian; Liu, Mengxi; Marinoni, Andrea; Liu, Xiaoping

doi:https://doi.org/10.5194/essd-15-555-2023

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.

https://doi.org/10.5194/essd-15-555-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 2

Data description paper

|

03 Feb 2023

Data description paper |

| 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, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu

Data sets

UGS-1m: Fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu https://doi.org/10.57760/sciencedb.07049

Model code and software

liumency/UGS-1m: v1.0 (v1.0) Mengxi Liu https://doi.org/10.5281/zenodo.7581694

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.