## 5 Conclusions  
This study focused on the urban green spaces located within the Fifth Ring Road of Beijing. Deep learning technology can be a feasible approach for information extraction and classification of urban green space. Herein, the characteristics of these green spaces were examined through three distinct dimensions: the fundamental quantitative attributes of urban green zones, the landscape configuration of park green areas, and the service capacity provided by these park green spaces. The conclusions are as follows: (1) Compared with the results of only SVM extraction, the edge of green space information extracted by using the deep learning U-net model developed in this study is more complete and can effectively separate green and non-green spaces in the city, reduce the influence of other feature information on the classification results, and improve the classification accuracy by 10.68%. (2) The service capacity of green zones is of greater significance than the landscape configuration and fundamental quantitative attributes of these areas. This suggests that in the development of urban green zones in China, an increasing emphasis has been placed on the service capacity of such spaces, followed by considerations of their landscape patterns, whilst the relevance of conventional quantitative metrics is progressively diminishing. (3) The extent of green spaces within the study area is deemed adequate. The overall evaluation classification grade is II. The service capacity level of green spaces is good, the landscape structure of green spaces is reasonable, and the evaluation grade is I. The comprehensive score of overall green space construction is 0.52, and the comprehensive evaluation grade is III, indicating the occurrence of serious landscape fragmentation and lack of landscape diversity.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16213946/s1](https://www.mdpi.com/article/10.3390/rs16213946/s1)]([https://www.mdpi.com/article/10.3390/rs16213946/s1](https://www.mdpi.com/article/10.3390/rs16213946/s1)), Schedule S1: Field questionnaire; Schedule S2: Statistical table of quasi-test layer; Schedule S3: Index layer score statistics table.  
Conceptualization, B.L. and X.X.; data curation, B.L. and X.X.; funding acquisition, B.L., X.X. and S.L. ([PERSON]); project administration, X.X., X.L. and S.L. ([PERSON]); supervision, B.L.; writing--original draft, H.W., Y.D., H.L. and C.L.; writing--review and editing, B.L., X.X., N.Z., S.L. ([PERSON]) and S.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Director's Foundation of Institute of Forestry and Pomology in Beijing Academy of Agriculture and Forestry Sciences (Grant No. LOSSZJJ202302), and Innovation Capacity Building of Beijing Academy of Agriculture and Forestry (Grant Nos. KJCX20230306 and KJCX20240701).  
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.  
The authors greatly acknowledge the support from the following units: Institute of Forestry and Pomology, Beijing of Agriculture and Forestry Sciences; Beijing Yanshan Forest Ecosystem and Research Station; and Forestry College of Shenyang Agricultural University.  
The authors declare no conflicts of interest.