Preprints
https://doi.org/10.5194/essd-2022-75
https://doi.org/10.5194/essd-2022-75
 
22 Jun 2022
22 Jun 2022
Status: this preprint is currently under review for the journal ESSD.

UGS-1m: Fine-grained urban green space mapping of 34 major cities in China based on the deep learning framework

Qian Shi1,2, Mengxi Liu1,2, Andrea Marinoni3,4, and Xiaoping Liu1,2 Qian Shi et al.
  • 1School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
  • 2Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China
  • 3Dept. of Physics and Technology, UiT the Arctic University of Norway, 9019 Tromsø, Norway
  • 4Dept. of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK

Abstract. Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. Although the development of remote sensing platforms and deep learning technologies have provided opportunities for UGS mapping from high-resolution images (HRIs), challenges still exist in its large-scale and fine-grained application, due to insufficient annotated datasets and specially designed methods for UGS. Moreover, the domain shift between images from different regions is also a problem that must be solved. To address these issues, a general deep learning (DL) framework is proposed for UGS mapping in the large scale, and the fine-grained UGS maps of 34 major cities/areas in China are generated (UGS-1m). The DL framework consists of a generator and a discriminator. The generator is a fully convolutional network designed for UGS extraction (UGSNet), which integrates attention mechanisms to improve the discrimination to UGS, and employs a point rending strategy for edge recovery. The discriminator is a fully connected network aiming to deal with the domain shift between images. To support the model training, an urban green space dataset (UGSet) with a total number of 4,454 samples of size 512×512 is provided. The main steps to obtain UGS-1m can be summarized as follows: a) Firstly, the UGSNet will be pre-trained on the UGSet in order to get a good starting training point for the generator; b) After pre-training on the UGSet, the discriminator is responsible to adapt the pre-trained UGSNet to different cities/areas through adversarial training; c) Finally, the UGS results of the 34 major cities/areas in China (UGS-1m) are obtained using 2,343 Google Earth images with a data frame of 7'30" in longitude and 5'00" in latitude, and a spatial resolution of nearly 1.1 meters. Evaluating the performance of the proposed approach on samples from Guangzhou city shows the validity of the UGS-1m products, with an overall accuracy of 87.4 % and an F1 score of 81.14 %. Furthermore, experiments on UGSet with the existing state-of-the-art (SOTA) DL models proves the effectiveness of UGSNet, with the highest F1 of 77.30 %. Finally, the comparisons with existing products further shows the feasibility of the UGS-1m and the effectiveness and great potential of the proposed DL framework. The UGS-1m can be downloaded from https://doi.org/10.5281/zenodo.6155516 (Shi et al., 2022).

Qian Shi et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-75', Anonymous Referee #1, 18 Jul 2022
    • AC1: 'Reply on RC1', Mengxi Liu, 20 Jul 2022
  • RC2: 'Comment on essd-2022-75', Robbe Neyns, 02 Sep 2022
    • AC2: 'Reply on RC2', Mengxi Liu, 05 Sep 2022

Qian Shi et al.

Data sets

UGS-1m: Fine-grained urban green space mapping of 34 major cities in China based on the deep learning framework Qian Shi, Mengxi Liu and Andrea Marinoni https://doi.org/10.5281/zenodo.6155516

Qian Shi et al.

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Short summary
A large-scale and high-resolution urban green space (UGS) maps of 34 major cities/areas in China (UGS-1m) are generated to provide basic UGS information, such as distribution, area and degree of fragmentation, for UGS research in relevant areas. Moreover, an urban green space dataset (UGSet) with a total number of 4,454 samples of size 512×512 are also supplied as the benchmark to support model training and comparison and help the development of UGS mapping algorithms.