Decoding the Role of Urban Green Space Morphology in Shaping Visual Perception: A Park-Based Study

Urban green spaces, vital public infrastructure, have received limited research on how their morphology affects visual perception preferences. Using data from ten parks, we generated green space maps from high-resolution satellite imagery and calculated indicators, such as quantity, fragmentation, connectivity, and shape complexity. By combining the Mask2Former image segmentation deep learning model with a multi-objective regression model and structural equation modeling, we analyzed the relationship between green space morphology and visual perception preferences, controlling for geographic and demographic factors. The results showed that green spaces with tighter connectivity, aggregation, continuity, and shape complexity led to more distinct visual perceptions. This relationship was mediated by the proportion of landscape elements. The distribution, shape, and connectivity of urban green spaces had an independent impact on individual visual perception, far exceeding the influence of quantity alone. The spatial morphology of urban green spaces should be incorporated into health-oriented urban space design, exploring the global interest in how green spaces impact urban human well-being, and providing valuable insights for urban green space planning and health-driven urban space design.