Mitigation of the heat island effect is critical due to the frequency of extremely hot weather. Urban street greening can achieve this mitigation and improve the quality of urban spaces and people’s welfare. However, a clear definition of street green space morphology is lacking, and the nonlinear mechanism of its cooling effect is still unclear; the interaction between street green space morphology and the surrounding built environment has not been investigated. This study used machine learning, deep learning, and computer vision methods to predict land surface temperature based on street green space morphology and the surrounding built environment. The performances of the XGBoost, LightGBM, and CatBoost models were then compared, and the nonlinear cooling effects offered by the street green space morphology were analyzed using the Shapley method. The results show that streets with a high level of green environment exposure (GVI > 0.4, NDVI > 4) can accommodate more types of green space morphology while maintaining the cooling effect. Additionally, the proportion of vegetation with simple geometry (FI < 0.2), large leaves (FD < 0.65), light-colored leaves (CSI > 13), and high leaf density (TDE > 3) should be increased in streets with a low level of green environment exposure (GVI < 0.1, NDVI < 2.5). Meanwhile, streets with highly variable building heights (AFI > 1.5) or large areas covered by buildings (BC > 0.3) should increase large leaf vegetation (FD < 0.65) while decreasing dark leaf vegetation (CSI < 13). The study uses machine learning methods to construct a nonlinear cooling benefit model for street green space morphology, proposes design recommendations for different street green spaces that consider climate adaptation, and provides a reference for urban thermal environment regulation.

Urban street space is a critical reflection of a city’s vitality and image and a critical component of urban planning. While visual perceptual information about an urban street space can reflect the composition of place elements and spatial relationships, it lacks a unified and comprehensive quantification system. It is frequently presented in the form of element proportions without accounting for realistic factors, such as occlusion, light and shadow, and materials, making it difficult for the data to accurately describe the complex information found in real scenes. The conclusions of related studies are insufficiently focused to serve as a guide for designing solutions, remaining merely theoretical paradigms. As such, this study employed semantic segmentation and information entropy models to generate four visual perceptual information quantity (VPIQ) measures of street space: (1) form; (2) line; (3) texture; and (4) color. Then, at the macro level, the streetscape coefficient of variation (SCV) and K-means cluster entropy (HCK) were proposed to quantify the street’s spatial variation characteristics based on VPIQ. Additionally, we used geographically weighted regression (GWR) to investigate the relationship between VPIQ and street elements at the meso level as well as its practical application. This method can accurately and objectively describe and detect the current state of street spaces, assisting urban planners and decision-makers in making decisions about planning policies, urban regeneration schemes, and how to manage the street environment.

Rapid urbanization presents policymakers and planners with the challenge of balancing public open spaces design with the conservation and improvement of natural resources. A comprehensive understanding of the land economic value of urban blue-green spaces (UBGS) holds immense significance for urban sustainable development, urban spatial justice and the promotion of human well-being. In this study, the MGWR model is employed to discuss the heterogeneous effects of UBGS on housing prices in Hangzhou. Additionally, the interaction effect between blue space and green space was examined at the district level, and the specific locations and spatial patterns were identified. The results show that (1) different types, features and accessibility of UBGS have different degrees and spatial scale of effect on housing prices, and will be affected by other attributes of UBGS; (2) in 30.92% of the main urban area of Hangzhou, the effect of blue spaces and green spaces on housing prices exhibits an interactive effect. The spatial patterns are divided into blue-green positive synergistic, antagonistic and negative synergistic regions; (3) green space has positive and negative effects on housing prices, while blue space has positive effects on housing prices at the regional level. The existence of water bodies can promote the positive effect of green spaces on housing prices or alleviate the negative effect. The results indicate that planners must transcend the singular focus on blue or green space planning and instead consider both in an integrated manner. This outcome can provide valuable references for UBGS planning.