Urban Street Greening in a Developed City: The Influence of COVID-19 and Socio-Economic Dynamics in Beijing

Liu Cui (), Hanwen Yang, Xiaoxu Heng, Ruiqi Song (), Lunsai Wu and Yike Hu
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Liu Cui: School of Landscape Architecture, Beijing Forestry University, Beijing 100091, China
Hanwen Yang: School of Architecture, Tianjin University, Tianjin 300072, China
Xiaoxu Heng: School of Architecture, Tianjin University, Tianjin 300072, China
Ruiqi Song: School of Architecture, Tianjin Renai College, Tianjin 301636, China
Lunsai Wu: School of Architecture, Tianjin University, Tianjin 300072, China
Yike Hu: School of Architecture, Tianjin University, Tianjin 300072, China

Land, 2025, vol. 14, issue 2, 1-19

Abstract: This study aims to investigate the spatial distribution and structural characteristics of urban greening in Beijing, focusing on three typologies: Single Tree (S-T), Tree–ush (T-B), and Tree–Bush–Grass (T-B-G). The analysis examines how socio-economic factors and the COVID-19 pandemic have influenced these structures across three time periods: pre-pandemic, during the pandemic, and post-pandemic recovery. To achieve this, a deep learning-based approach utilizing the DeepLabV3+ neural network was applied to analyze the features extracted from Baidu Street View (BSV) images. This method enabled the precise quantification of the structural characteristics of urban greening. The findings indicate that greening structures are significantly influenced by commercial activity, population mobility, and economic conditions. During the pandemic, simpler forms like S-T proved more resilient due to their lower maintenance requirements, while complex systems such as T-B-G experienced reduced support. These results underscore the vulnerability of green infrastructure during economic strain and highlight the need for urban greening strategies that incorporate flexibility and resilience to adapt to changing socio-economic contexts while maintaining ecological and social benefits.

Keywords: urban street greening; green infrastructures; street view images; COVID-19 pandemic; computer vision (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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