Deep exploration of street view features for identifying urban vitality: A case study of Qingdao city

Abstract

Urban vitality has significant practical implications for urban management and planning. In this study, we propose a comprehensive research framework that combines street scene images, point of interest (POI) data, road network data, and residential land data, and employs deep learning algorithms to explore the characteristics and influencing factors of urban vitality from a social perception perspective. By designing multi-scale semantic segmentation models, emotion perception models, and street perception models, we deeply explore the street features of the city. At the block level, we use weighted calculation methods to quantify urban vitality by combining POI data and residential land data, accurately characterizing the city. Finally, we analyze the driving factors of urban vitality using random forest and SHAP methods. The research results show that Chengyang District and Laoshan District have advantages in visual perception, while Shibei District and Shinan District exhibit advantages in urban vitality. The overall urban vitality in the main urban area of Qingdao City is low, with high scores in emotion perception and visual perception, but low scores in transportation accessibility and facility convenience. Visual perception factors play a significant role in urban vitality, highlighting the importance of urban street beautification and humanized design in economic development and environmental construction. The analytical results of this study contribute to optimizing urban spatial features and provide references for urban planning and construction.

Keywords

• Street view image
• Deep learning algorithms
• Semantic segmentation
• SHAP method
• Social perception
• Urban vitality