Uncovering the structural influence of urban park landscapes on psychological restoration via graph learning

Over half of global population now resides in urban areas, where mental health challenges such as anxiety and depression are increasingly prevalent. Urban park landscapes are recognized as critical settings for promoting psychological restoration, yet a systematic understanding of what makes these spaces restorative remains limited. In this paper, we propose a novel graph learning-based framework that models and interprets the perceptual structure of urban park landscapes in relation to their restorative potential. The framework comprises two key components: a predictive module that quantitatively estimates perceived restorativeness based on scene-level visual and spatial features, and an interpretive module that identifies the importance of individual landscape elements and their spatial configurations. To support this approach, we introduce a novel dataset of urban park landscape images annotated with semantic, structural, and perceptual labels, facilitated by a large language model–assisted annotation pipeline. Experimental results demonstrate our framework’s predictive accuracy and its ability to provide interpretable visual explanations grounded in environmental psychology. Our findings offer actionable guidance for urban planners and landscape designers aiming to create psychologically supportive public environments.