Effects of Street-Level Visual Perception on Different Types of Leisure Activity Intensity in Waterfront Spaces: A Case Study of the Core Section of the Pearl River, Guangzhou

As urban waterfront public spaces have increasingly become important settings for residents’ daily leisure activities, there remains a lack of empirical evidence based on objective image data regarding how street-level visual environments influence different types of leisure activities. The existing studies have largely relied on macro-scale built environment indicators and paid limited attention to micro-scale visual perception from the pedestrian perspective. To address this gap, this study focuses on the core waterfront section of the Pearl River in Guangzhou. Behavioral observations were conducted across nine spatial units during different time periods on weekdays and weekends, yielding 54 samples of passive, active, and social activity intensity. Meanwhile, 109 street-view sampling points were established, generating 436 pedestrian-view images. Using Mask2Former with an ADE20K pre-trained model, visual environmental indicators—including the Green View Index (GVI), Sky View Index (SVI), built environment proportion, road proportion, and visual diversity (Entropy)—were extracted. Spearman correlation and multiple linear regression were applied to examine their effects on activity intensity. The results show that leisure activities are generally more active in the evening and on weekends, with social activities exhibiting the strongest temporal variation. Active activities remain relatively stable, passive activities show temporal dependence, and social activities display localized high-intensity clustering. Regression results reveal differentiated environmental responses: visual diversity has a stable positive effect on passive activities, active activities show weak associations with visual variables, and social activities are the most sensitive, with GVI, SVI, and built proportion showing significant negative effects, while visual diversity shows a significant positive effect. The social activity model also demonstrates the highest explanatory power (Adj. R2 = 0.488). Overall, this study develops a street-view semantic segmentation-based method for quantifying waterfront visual environments, demonstrates the critical role of visual environmental composition in shaping activity patterns, and provides empirical support for the fine-grained and activity-oriented optimization of waterfront public spaces.