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

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.