As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments.

Amid global warming and urbanization, outdoor thermal comfort has become a critical consideration in landscape architecture. This study integrates a systematic review and bibliometric analysis of 1417 empirical studies (1980–2024) sourced from Web of Science, aiming to clarify the current state of research, identify core themes, and propose future directions. This study examines key evaluation models, the influence of spatial morphology, and their practical applications using keyword co-occurrence, citation networks, and thematic analyses. Findings show a significant rise in research over the past decade, particularly in tropical and subtropical regions. Core themes include thermal comfort indices (PMV, PET, and UTCI), microclimate regulation, and important spatial indicators (height-to-width ratio, sky view factor, and greening). The field is increasingly shifting towards simulation tools (such as ENVI-met and CFD) rather than traditional field measurements, with artificial intelligence emerging as a tool for predictive and regulatory purposes, though its application remains limited. However, much of the research focuses on small-scale morphological optimization and lacks a systematic framework for spatial representation. Future research should prioritize developing a comprehensive evaluation system adaptable to diverse landscapes, investigating the interplay between spatial form and thermal comfort, and advancing sustainable, low-carbon design strategies. The insights from this study provide a solid foundation for improving outdoor thermal comfort and guiding sustainable urban development through landscape architecture.

The urban built environment and sociodemographic characteristics have complex relationships with urban crime. However, previous studies have had limitations such as generalizing urban green space types, urban functionality, and sociodemographic characteristics. Given these, this study aimed to explore the relationship between them using more detailed indicators. The study utilized Google Street View and points of interest to depict the built environment. Building on previous work that segmented natural and artificial elements in streetscape images, this study further distinguished trees, bush, and grass. Additionally, it incorporated data from the Data Analysis and Visualization Unit of the DC Office of Planning to reflect a broader range of individual characteristics. Weighted least squares regression and Pearson correlation analysis were used to test the relationship between the built environment, sociodemographic, and crime, respectively. Some of the key findings are as follows. (1) Trees, bushes, and grass all reduce crime. (2) Urban functionality is hard to curb crime by enhancing informal public surveillance. (3) Among the sociodemographic variables, the walking commute rate is the variable most strongly positively correlated with crime. (4) Family relationships play an important role in suppressing crime. This study examined a more comprehensive range of indicators affecting urban crime in favor of safer cities.

Street design quality and socio-economic factors jointly influence housing prices, but their intertwined effects and spatial variations remain under-quantified. Housing prices not only reflect residents’ neighborhood experiences but also stem from the spillover value of public streets perceived and used by different users. This study takes Tianjin as a case and views the street environment as an immediate experience proxy for through-travelers, combining street view images and crowdsourced perception data to extract both subjective and objective indicators of the street environment, and integrating neighborhood and location characteristics. We use Geographical-XGBoost to evaluate the relative contributions of multiple factors to housing prices and their spatial variations. The results show that incorporating both subjective and objective street information into the Hedonic Pricing Model (HPM) improves its explanatory power, while local modeling with G-XGBoost further reveals significant heterogeneity in the strength and direction of effects across different locations. The results indicate that incorporating both subjective and objective street information into the HPM enhances explanatory power, while local modeling with G-XGBoost reveals significant heterogeneity in the strength and direction of effects across different locations. Street greening, educational resources, and transportation accessibility are consistently associated with higher housing prices, but their strength varies by location. Core urban areas exhibit a “counterproductive effect” in terms of complexity and recognizability, while peripheral areas show a “barely acceptable effect,” which may increase cognitive load and uncertainty for through-travelers. In summary, street environments and socio-economic conditions jointly influence housing prices via a “corridor-side–community-side” dual-pathway: the former (enclosure, safety, recognizability) corresponds to immediate improvements for through-travelers, while the latter (education and public services) corresponds to long-term improvements for residents. Therefore, core urban areas should control design complexity and optimize human-scale safety cues, while peripheral areas should focus on enhancing public services and transportation, and meeting basic quality thresholds with green spaces and open areas. Urban renewal within a 15 min walking radius of residential areas is expected to collaboratively improve daily travel experiences and neighborhood quality for both residents and through-travelers, supporting differentiated housing policy development and enhancing overall quality of life.

Street greening is widely recognized as influencing resident well-being and housing prices, and street-view imagery provides a fine-grained data source for quantifying urban microenvironments. However, existing research predominantly relies on single indicators such as the Green View Index (GVI) and overall green coverage/volume lacking a systematic analysis of how the hierarchical structure of trees, shrubs, and grass relates to housing prices. This study examines the high-density block context of Tianjin’s six urban districts. Using the Street Greening Space Structure (SGSS) dataset to construct greening structure configurations, we integrate housing-price data, neighborhood attributes, and 13,280 street-view images from the study area. We quantify how “visibility and hierarchical ratios” are capitalized on in the housing market and identify auditable threshold ranges and contextual gating. We propose an urban–forest structural system centered on visibility and hierarchical ratios that links street-level observability to ecosystem services. Employing an integrated framework combining Geographical-XGBoost (G-XGBoost) and SHapley Additive exPlanations (SHAP), we move beyond average effects to reveal structural detail and contextual heterogeneity in capitalization. Our findings indicate that tree visibility G_TVI is the most robust and readily capitalized price signal: when G_TVI increases from approximately 0.06 to 0.12–0.16, housing prices rise by about 8%–10%. Hierarchical structure is crucial: balanced tree–shrub ratios and moderate shrub–grass ratios translate “visible green” into functional green. Capitalization effects are environmentally conditioned—more pronounced along corridors with high centrality and accessibility—and are likewise common in dense East Asian metropolises (e.g., Beijing, Shanghai, Seoul, and Tokyo) and rapidly motorizing cities (e.g., Bangkok and Jakarta). These patterns suggest parametric prescriptions that prioritize canopy-corridor continuity and keep ratios within actionable threshold bands. We translate these findings into urban greening strategies that prioritize canopy continuity, under-canopy permeability, and maintainability, providing sustainability-oriented, parameterized guidance for converting urban greening structure into ecological capital for sustainable cities.

Understanding solar radiation in urban street spaces is crucial for comprehending residents’ environmental experiences and enhancing their quality of life. However, existing studies rarely focus on the patterns of urban street solar radiation over time and across different urban and suburban areas. In this study, street view images from the summers of 2013 and 2019 in Shanghai were used to calculate solar radiation in urban street spaces. The results show a general decrease in street solar radiation in 2019 compared to 2013, with an average drop of 12.34%. The decrease was most significant in October (13.47%) and least in May (11.71%). In terms of solar radiation data gathered from street view sampling points, 76.57% showed a decrease, while 23.43% showed an increase. Spatially, solar radiation decreased by 79.66% for every additional 1.5 km from the city centre. In summary, solar radiation generally shows a decreasing trend, with significant variations between different areas. These findings are vitally important for guiding urban planning, optimising green infrastructure, and enhancing the urban ecological environment, further promoting sustainable urban development and improving residents’ quality of life.