Dynamic Assessment of Street Environmental Quality Using Time-Series Street View Imagery Within Daily Intervals

Puxuan Zhang, Yichen Liu and Yihua Huang ()
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Puxuan Zhang: Shanghai Academy of Fine Arts, Shanghai University, Shangda Road No. 99, Shanghai 200444, China
Yichen Liu: Shanghai Academy of Fine Arts, Shanghai University, Shangda Road No. 99, Shanghai 200444, China
Yihua Huang: Shanghai Academy of Fine Arts, Shanghai University, Shangda Road No. 99, Shanghai 200444, China

Land, 2025, vol. 14, issue 8, 1-19

Abstract: Rapid urbanization has intensified global settlement density, significantly increasing the importance of urban street environmental quality, which profoundly affects residents’ physical and psychological well-being. Traditional methods for evaluating urban environmental quality have largely overlooked dynamic perceptual changes occurring throughout the day, resulting in incomplete assessments. To bridge this methodological gap, this study presents an innovative approach combining advanced deep learning techniques with time-series street view imagery (SVI) analysis to systematically quantify spatio-temporal variations in the perceived environmental quality of pedestrian-oriented streets. It further addresses two central questions: how perceived environmental quality varies spatially across sections of a pedestrian-oriented street and how these perceptions fluctuate temporally throughout the day. Utilizing Golden Street, a representative living street in Shanghai’s Changning District, as the empirical setting, street view images were manually collected at 96 sampling points across multiple time intervals within a single day. The collected images underwent semantic segmentation using the DeepLabv3+ model, and emotional scores were quantified through the validated MIT Place Pulse 2.0 dataset across six subjective indicators: “Safe,” “Lively,” “Wealthy,” “Beautiful,” “Depressing,” and “Boring.” Spatial and temporal patterns of these indicators were subsequently analyzed to elucidate their relationships with environmental attributes. This study demonstrates the effectiveness of integrating deep learning models with time-series SVI for assessing urban environmental perceptions, providing robust empirical insights for urban planners and policymakers. The results emphasize the necessity of context-sensitive, temporally adaptive urban design strategies to enhance urban livability and psychological well-being, ultimately contributing to more vibrant, secure, and sustainable pedestrian-oriented urban environments.

Keywords: street environmental quality; time-series street view imagery; pedestrian-oriented streets; perceptual analysis; urban design (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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