Deep Learning-Based Image Recognition for Spatial Perception Assessment and Landscape Optimization in Urban Parks

The visual environment of urban parks plays a critical role in shaping public spatial perception and environmental experience; however, systematic understanding of age-related differences and nonlinear responses to visual environments remains limited. Taking Guazhu Lake Park in Shaoxing, China, as the study area, this study developed an integrated analytical framework combining deep learning-based image recognition, spatial perception assessment, machine learning prediction, and SHAP-based interpretation. Eight visual environmental indicators, including the Green View Index (GVI), Sky View Index (SVI), water proportion, hardscape proportion, facility visibility, and visual obstruction, were extracted using semantic segmentation. These indicators were integrated with evaluations of beauty, safety, restoration, and vibrancy from children, adults, and older adults to predict spatial perception using machine learning models. SHAP analysis was further employed to identify the relative importance and nonlinear responses of key visual attributes. The results showed that the visual environment of Guazhu Lake Park exhibited pronounced spatial heterogeneity, characterized by abundant vegetation and water features, together with a coexistence of open and enclosed spaces. Different visual attributes exerted differentiated and nonlinear effects on spatial perception, while distinct age groups exhibited substantially different sensitivities to visual environmental characteristics. Based on these findings, low-perception areas were identified, and age-specific micro-landscape optimization strategies were proposed. This study provides a data-driven approach and practical basis for quantitative spatial perception assessment and age-inclusive landscape optimization in urban parks.