Traditional urban landscape evaluations have primarily relied on either objective spatial metrics, such as the Green View Index (GVI), or subjective human surveys, often failing to capture the complex mechanisms of human environmental perception. This study proposes a novel Explainable Artificial Intelligence (XAI) framework that integrates objective physical configuration with subjective cognitive assessment to predict human landscape preference. Utilizing 159 urban landscape images, we extracted physical features via semantic segmentation (SegFormer) and psychological perceptions via a zero-shot vision-language model (CLIP). Our hybrid Random Forest model successfully bridged these dimensions, achieving moderate yet promising predictive performance (Rsquare = 0.442). SHAP (Shapley Additive exPlanations) analysis revealed that psychological perceptions—specifically Safety (0.104), Fascination (0.096), and Tranquility (0.080)—outperformed traditional objective metrics like GVI (0.067) in determining overall preference, while sub-model interpretation linked these psychological responses to specific physical elements such as buildings, sky openness, low vegetation, and water bodies. The findings suggest that urban green space design should move beyond maximizing greenery quantity and instead prioritize spatial compositions that induce psychological security, visual interest, and restoration. The proposed framework offers a scalable and interpretable tool for human-centered landscape assessment, while acknowledging limitations related to sample size, cultural generalizability, pretrained model bias, and reliance on static two-dimensional imagery.
Kang et al. (Fri,) studied this question.