Search Results (65)

High eye-level greenness could bring multiple health benefits, and multiple kinds of street view images (SVI) have been widely used in measuring green view index (GVI). However, how street view image-based GVI (GVI_SVI) aligns with pedestrians’ perspective remains unknown. This study compared the GVI_SVI calculated using different SVI (view angle of 60°, view angle of 90°, panoramic) with the GVI computed using on-site taken photos imitating pedestrians’ perspective. The influences of road width and road greenery level are also examined in the comparison. The study was conducted in Yangpu district, Shanghai, China, and 194 sampling points on different types of roads with various greenery levels were involved in the study. The results indicated that GVI_SVI is significantly correlated to that from pedestrians’ perspective. GVI_SVI based on the SVI with a 60° field of view shows the smallest differences from that of pedestrians’ perspective. GVI_SVI for front and back views is closer to GVI of pedestrian’s perspective. GVI on wide roads, and streets with higher levels of greenery are more likely to be underestimated by GVI_SVI. This study explored how GVI_SVI may represent GVI from the pedestrians’ perspective as well as identified related factors. This study could provide valuable insights for the application of street view images in measuring street GVI. Full article

Deep learning-powered Street View Imagery (SVI) analytics provides a critical mechanism for smart city perception within the framework of Sustainable Development Goal 11 (SDG 11), effectively bridging the gap left by traditional remote sensing in fine-grained street-level observation. Over the years, deep learning-based semantic segmentation of urban streetscapes has become the dominant paradigm. However, when scaling to megacity measurements, current research faces the dual bottlenecks of “computational redundancy” and the “geographical domain shift” caused by the blind application of pre-trained models based on Western datasets. To address these challenges, this study is the first to systematically quantify the performance trade-off between Multi-Task Learning (MTL) and Single-Task Learning (STL) in megacity scenarios. Using this as a baseline, we constructed and validated a “low-computation, high-robustness” framework for streetscape semantic perception and spatial measurement. Relying on an integrated ResNeXt101-FPN MTL architecture and an ultra-low-cost fine-tuning strategy to overcome geographical domain shift, we extracted and analyzed the spatial heterogeneity of five core semantic elements—vegetation, sky, building, road, and vehicle—across the road network within Beijing’s 5th Ring Road. The results indicate the following: (1) We explicitly defined the computation-accuracy trade-off of MTL and STL in megacity perception. While utilizing only 1/5 of the parameters of STL, the MTL framework achieved a 5.34-fold increase in inference speed with a negligible 0.1% loss in overall mean Intersection over Union (mIoU); however, a 27.13% decrease in boundary segmentation accuracy was observed. (2) We established a low-cost, localized correction paradigm to overcome domain shift. Utilizing a minimal annotation cost (only 200 local images) significantly improved cross-domain adaptability, boosting the overall mIoU by 8.92% and significantly mitigating the geographical domain shift problem. (3) Multi-dimensional measurement and spatial analysis revealed a significant spatial decoupling pattern in Beijing’s streetscapes. The visual proportion of vegetation exhibited a pronounced “north-high, south-low” spatial differentiation, whereas built environment elements (e.g., building and road) displayed a typical “center-periphery” concentric gradient. This objectively reflects the spatial inequality of urban street greenery resources and the monocentric development characteristics of the built environment. The proposed framework therefore serves as a low-cost, AI-driven computational paradigm for smart city perception in resource-constrained regions. Furthermore, the revealed spatial heterogeneity offers data-driven insights for formulating sustainable urban renewal policies aligned with SDG 11. Full article

Perceived street vitality directly reflects residents’ assessments of the attractiveness of the street environment; it is not only an important focus of urban vitality research but also closely related to human-centred sustainable urban development. However, limited data availability and the complexity of urban environments have constrained fine-grained spatial analysis at the city scale. To address this issue, this study quantified perceived street vitality by collecting street-view imagery, extracting streetscape features, and integrating these data with questionnaire survey results. After comparing multiple models, a geographically weighted machine learning model was employed to identify key visual predictors, model-estimated marginal associations, interaction patterns, and spatial heterogeneity related to perceived street vitality. The results show that areas with high perceived street vitality are mainly located along street segments with abundant greenery and open spaces, whereas low-value areas are concentrated in densely built and enclosed environments. Among the various streetscape elements, buildings, vegetation, and sky are the key visual elements most strongly associated with perceived street vitality. A model incorporating these elements accounted for 67.2% of the variance in perceived street vitality. Notably, the strength of these associations varied significantly across different areas. This study provides empirical evidence and evidence-based support for sustainable urban renewal, the optimisation of street-space layouts in high-density urban areas, and the improvement in street environmental quality. Full article

In the context of urban renewal, the spatial interface of historic commercial districts functions as both a carrier of historical character and a key setting for commercial activity, public life, and local cultural expression. To address the limitations of conventional studies that rely heavily on field observation and qualitative description, this study takes Xiaohe Zhijie in Hangzhou as a case and develops a multi-level street-view recognition framework for the quantitative analysis of spatial interface characteristics. Based on street-view image collection and standardized preprocessing, a sample database was established at the sampling-point scale. Semantic segmentation, automated commercial object detection, and manual interpretation were combined to identify interface elements, including buildings, sky, greenery, pavement, vehicles, pedestrians, and commercial objects, while commercial content was assessed in terms of locality and homogenization. The results show that Xiaohe Zhijie exhibits a building-dominated and relatively enclosed interface pattern, with greenery and pavement forming the basic environmental ground, weak vehicle interference, and localized enhancement of vitality through commercial objects and pedestrian activities. Significant differences were found among street segments in openness, commercial coverage, and local expression. Three interface types were identified: commercial–cultural composite, local life-oriented, and waterfront landscape–cultural composite. The main challenge lies not in commercialization itself, but in stronger visual locality than content locality and increasing homogenization, resulting in a pattern of “localized form but homogenized content.” Full article

As urbanization intensifies, improving street thermal comfort has become a critical issue in urban renewal. While existing studies generally assume that increasing the Green View Index (GVI) linearly improves pedestrian thermal comfort, this study identifies a significant “Decoupling Effect” in high-density commercial areas through field measurements and numerical simulations of three typical street types (commercial–service, ecological–recreational, and historical–cultural) in Shenyang. Integrating DeepLab V3 semantic segmentation with ENVI-met version 5.1.1 microclimate simulation, the results demonstrate a robust monotonic negative correlation between GVI and Physiological Equivalent Temperature (PET) in ecological streets (Spearman’s ρ = −0.692, p < 0.001), confirming the consistent cooling benefit of greenery in nature-dominated environments. However, a distinct “Threshold Effect” was identified in commercial streets using Piecewise Linear Regression (PLR). A critical breakpoint was detected at GVI = 22.08%. Below this threshold, visual greenery effectively contributes to cooling (slope = −0.454); yet, once GVI exceeds 22.08%, the cooling efficacy diminishes significantly (slope = −0.109), marking the onset of a “decoupling” phase. Specifically, despite Wenhua Road achieving a GVI of ~24.5% with a complex “three-board, four-belt” structure, its PET peak reaches 46.15 °C, approximately 5.5 °C higher than ecological streets. Mechanism analysis reveals that under peak thermal stress (Traffic Heat ≈ 75 W/m²), the high-intensity anthropogenic heat and hardscape radiation exceed the evaporative cooling threshold of vegetation. This study reveals the non-linear relationship between visual greenery and the physical thermal environment, suggesting that simply pursuing visual green quantity is ineffective in commercial canyon renewal; instead, a threshold-based synergistic optimization of canopy shading and pavement thermal performance is required. These findings provide a quantitative basis for sustainable street landscape planning and urban climate adaptation strategies in high-density cities. Full article

In high-density urban cores, pedestrian safety perception is shaped not only by street physical environments but also by pronounced spatial heterogeneity. However, existing studies often rely on global regression or small-sample surveys, making it difficult to simultaneously reveal city-scale regularities and localized mechanisms. Taking Futian District, Shenzhen, as a case study, this study develops an integrated analytical framework that combines street-view imagery, machine learning, and multiscale geographically weighted regression (MGWR) to measure pedestrian safety perception at the city scale and to unpack its spatial mechanisms. The results show that model explanatory power improves markedly after accounting for spatial non-stationarity, indicating strong context dependence in the formation of pedestrian safety perception. MGWR further reveals clear multiscale differentiation across streetscape visual elements: greenery-related elements (e.g., tree and plant) exhibit near-global and consistently positive effects, whereas traffic exposure and interface-related elements (e.g., car, road, and wall) operate more locally, with both the direction and magnitude of their effects varying substantially with neighborhood structure and traffic contexts. These findings suggest that the impacts of individual street elements on pedestrian safety perception are not universally transferable and should be interpreted within a spatial-scale and contextual framework. By integrating machine learning-based prediction with MGWR-based spatial interpretation, this study enables both efficient city-scale measurement and multiscale mechanism identification of pedestrian safety perception, providing empirical support for safety perception-oriented street planning and fine-grained urban design. Full article

China’s cities are shifting from expansion to renewal. In cold-climate cities, street use often drops in winter, so human-centred street quality matters. However, few studies measure behavioural activity using the same indicators for winter and non-winter periods. We propose an “activity portraits” framework and an Activity Portrait Index (API) that combines activity density, activity-type propensity, and age-group diversity. We used street-view images and field surveys from 37 street segments in Harbin, covering commercial, living, and landscape streets. Streetscape elements were extracted, and separate regression models were built for winter and non-winter conditions. In winter, seven streetscape factors were significant and were entered into the regression as four PCA components. In non-winter conditions, the model retained four predictors: street width-to-height (W/H) ratio, road clutter, greenery visibility, and signage density (R² = 0.802). The models show season-specific links between activity and street space and support a seasonal API measurement system for cold-region streets. The results inform targeted design and renewal principles to improve street usability and vitality year-round. Full article

Street livability is widely recognized as a fundamental indicator of urban livability. Despite growing global agendas advocating human-centered, sustainable, and smart cities, the microscale implementation of streetscape interventions remains limited and non-integrated. This gap is particularly evident in developing cities’ contexts where policy-level frameworks fail to translate into tangible street-level transformations. Responding to this challenge, this paper investigates how streetscape components can enhance everyday street livability. The study aims to explore opportunities for improving street livability through the utilization of three core streetscape components: vegetation, street furniture, and lighting. The discourse on street livability identifies vegetation, street furniture, and lighting as the primary drivers of high-quality urban spaces. Scholarly research suggests that these micro-interventions are most effective when viewed through the combined lenses of human-centered design, environmental sustainability, and smart city technology. While the literature indicates that integrating climate-responsive greenery and renewable energy systems can enhance social interaction and safety, it also highlights significant implementation hurdles. Specifically, researchers point to policy limitations, technical feasibility in developing nations, and the socio-economic threat of green gentrification. Despite these complexities, microscale streetscape improvements remain a vital strategy for fostering inclusive and resilient cities. Full article

Rapid urbanization has intensified the demand for street designs that reconcile ecological quality with positive human experiences, particularly in high-density cities such as Tianjin, China. Streets function as key interfaces where ecological processes, social activities and human perception intersect. However, existing research tends to emphasize the amount of greenery while overlooking its structural characteristics, to treat perception as a psychological response decoupled from spatial context, and to make limited use of fine-grained functional data to examine how ecology and perception interact. This study develops an integrated analytical framework that combines the DeepLabV3+ model to extract the Urban Street Greenery Generalized Structure (USGGS) from Baidu Street View imagery with a vision transformer model trained on the Place Pulse 2.0 dataset to derive multidimensional perceptual metrics. Functional diversity is represented using point-of-interest (POI) data, and an enhanced Light Gradient Boosting Machine (LightGBM) model is employed to explore associations among greenery structure, perceived qualities and functional characteristics. Analyses of six urban districts in Tianjin indicate that ecological and perceived street qualities are closely related to the degree of coupling between vegetation structure and functional diversity. Streets characterized by multi-layered greenery and diverse, active functions tend to exhibit higher perceived aesthetics, safety and vitality, whereas streets with single-layer vegetation or functionally monotonous environments generally do not perform as well. Functional patterns appear to mediate relationships between greening and perception by shaping how ecological form is experienced through everyday social activities. Overall, the results suggest that closer coordination between ecological design and functional organization is important for fostering urban streets that combine environmental resilience with strong perceived appeal. Full article

Current assessments of urban green spaces (UGS) rely largely on two-dimensional (2D) indicators, which fail to capture the three-dimensional (3D) structure necessary for evaluating ecological functions and human exposure. Among these, the Normalized Difference Vegetation Index (NDVI) describes top-down canopy greenness from a nadir perspective, whereas the Green View Index (GVI) quantifies vegetation visibility at street level from a pedestrian perspective. Because the relationship between NDVI and GVI remains unclear, multi-indicator assessments become difficult to interpret, limiting their ability to jointly characterize urban greenery. To address these gaps, we develop a synergy framework that integrates remote sensing with street-view images. First, we aligned the observation scales through street-view depth estimation and converted NDVI into fractional vegetation cover (FVC) through nonlinear mapping to unify measurement units. Correlation experiments revealed that the consistency between GVI and FVC was weak across the city (R² = 0.27) but substantially stronger along arterial roads with continuous vegetation (R² = 0.61). On this basis, we design a Green Synergy Index (GSI) that combines FVC and GVI using fractional power-law adjustments and an interaction term to capture their joint effects. Robustness tests indicate that GSI effectively handles extreme or mismatched cases, differentiates greening patterns, and integrates complementary information from nadir and street views without numerical instability. Furthermore, we assess the consistency between GSI and land surface temperature (LST), showing that the proposed index improves explanatory power compared with FVC and GVI alone (by 5.6% and 8.8%, respectively). Application to the study area yields a mean GSI value of 0.44 on a 0–1 scale, with spatial variations closely associated with road geometry and functional zoning. This enables the identification of mismatched canopy and visibility segments and supports targeted, climate-sensitive green infrastructure planning. Full article

Historic districts, as important spatial units that carry urban cultural memory and everyday social life, play a crucial role in shaping residents’ spatial identity, emotional attachment, and perceptual experience. Although quantitative research on built environments and perception has advanced considerably in recent years, the mechanisms through which perception is formed in historic districts, particularly the nonlinear threshold effects and perceptual trade-off patterns that arise under conditions of high-density and mixed land use, remain insufficiently examined. To address this gap, this study develops an analytical framework that integrates spatial attributes with multidimensional subjective perceptions. Focusing on six historic districts in central Shanghai, the study combines micro-scale environmental indicators extracted from street-view imagery, POI data, and public perceptual evaluations and employs an XGBoost model to identify the nonlinear response patterns, threshold effects, and perceptual trade-offs across seven perceptual dimensions. The results show that natural elements such as visual greenery and sky openness generate significant threshold-based enhancement effects, and once reaching a certain level of visibility, they substantially increase positive perceptions including beauty, safety, and cleanliness. By contrast, commercial and traffic-related facilities exhibit dual and competing perceptual influences. Moderate densities enhance liveliness, whereas high concentrations tend to induce perceptual fatigue and intensify negative emotional responses. Overall, perceptual quality in historic districts does not arise from linear accumulation but is shaped by dynamic perceptual trade-offs among natural features, functional elements, and cultural symbolism. Overall, the study reveals the coupling mechanism between spatial renewal and perceptual experience amid the pressures of urban modernization. It also demonstrates that increasing visible greenery (e.g., planting street trees, incorporating micro-green spaces, improving façade greening), enhancing street openness (e.g., optimizing view corridors, reducing visual obstruction, implementing moderate setback adjustments), guiding a moderate mix and spatial distribution of commercial and service functions, and strengthening the perceptibility of cultural landscape elements (e.g., façade restoration, streetscape coordination, and improved signage systems) are concrete and effective planning and design actions for improving landscape quality and enhancing the experiential quality of historic districts. Full article

Using Shanghai as a case study, this paper develops a multi-source fusion and interpretable machine learning framework. Sentiment indices were extracted from Weibo check-ins with ERNIE 3.0, street-view elements were identified using Mask2Former, and urban indicators like the Normalized Difference Vegetation Index, floor area ratio, and road network density were integrated. The coupling between residents’ sentiments and streetscape features during heatwaves was analyzed with Extreme Gradient Boosting, SHapley Additive exPlanations, and GeoSHAPLEY. Results show that (1) the average sentiment index is 0.583, indicating a generally positive tendency, with sentiments clustered spatially, and negative patches in central areas, while positive sentiments are concentrated in waterfronts and green zones. (2) SHapley Additive exPlanations analysis identifies NDVI (0.024), visual entropy (0.022), FAR (0.021), road network density (0.020), and aquatic rate (0.020) as key factors. Partial dependence results show that NDVI enhances sentiment at low-to-medium ranges but declines at higher levels; aquatic rate improves sentiment at 0.08–0.10; openness above 0.32 improves sentiment; and both visual entropy and color complexity show a U-shaped relationship. (3) GeoSHAPLEY shows pronounced spatial heterogeneity: waterfronts and the southwestern corridor have positive effects from water–green resources; high FAR and paved surfaces in the urban area exert negative influences; and orderly interfaces in the vitality corridor generate positive impacts. Overall, moderate greenery, visible water, openness, medium-density road networks, and orderly visual patterns mitigate negative sentiments during heatwaves, while excessive density and hard surfaces intensify stress. Based on these findings, this study proposes strategies: reducing density and impervious surfaces in the urban area, enhancing greenery and quality in waterfront and peripheral areas, and optimizing urban–rural interfaces. These insights support heat-adaptive and sustainable street design and spatial governance. Full article

As a key indicator for measuring urban green visibility, the Green View Index (GVI) reflects actual visible greenery from a human perspective, playing a vital role in assessing urban greening levels and optimizing green space layouts. Existing studies predominantly rely on single-source remote sensing image analysis or traditional statistical regression methods such as Ordinary Least Squares and Geographically Weighted Regression. These approaches struggle to capture spatial variations in human-perceived greenery at the street level and fail to identify the non-stationary effects of different drivers within localized areas. This study focuses on the Luolong District in the central urban area of Luoyang City, China. Utilizing Baidu Street View imagery and semantic segmentation technology, an automated GVI extraction model was developed to reveal its spatial differentiation characteristics. Spearman correlation analysis and Multiscale Geographically Weighted Regression were employed to identify the dominant drivers of GVI across four dimensions: landscape pattern, vegetation cover, built environment, and accessibility. Field surveys were conducted to validate the findings. The Multiscale Geographically Weighted Regression method allows different variables to have distinct spatial scales of influence in parameter estimation. This approach overcomes the limitations of traditional models in revealing spatial non-stationarity, thereby more accurately characterizing the spatial response mechanism of the Global Vulnerability Index (GVI). Results indicate the following: (1) The study area’s average GVI is 15.24%, reflecting a low overall level with significant spatial variation, exhibiting a “polar core” distribution pattern. (2) Fractal dimension, normalized vegetation index (NDVI), enclosure index, road density, population density, and green space accessibility positively influence GVI, while connectivity index, Euclidean nearest neighbor distance, building density, residential density, and water body accessibility negatively affect it. Among these, NDVI and enclosure index are the most critical factors. (3) Spatial influence scales vary significantly across factors. Euclidean nearest neighbor distance, building density, population density, green space accessibility, and water body accessibility exert global effects on GVI, while fractal dimension, connectivity index, normalized vegetation index, enclosure index, road density, and residential density demonstrate regional dependence. Field survey results confirm that the analytical conclusions align closely with actual greening conditions and socioeconomic characteristics. This study provides data support and decision-making references for green space planning and human habitat optimization in Luoyang City while also offering methodological insights for evaluating urban street green view index and researching ecological spatial equity. Full article

This investigation focuses on urban ornamental greenery, a field of research that is still relatively unexplored in Italy but is becoming increasingly important both from a botanical point of view and in relation to sustainable land management and planning. A checklist of the ornamental vascular flora of Basilicata (Southern Italy) is reported here. A total of 281 taxa were recorded, including trees, shrubs, herbaceous plants, and succulents cultivated in parks, gardens, and street trees. Such taxa (including 265 species s. str., 6 varieties, 5 subspecies, and 11 forms) belong to 201 genera, included in 94 families, among which the most represented are Rosaceae, Oleaceae, Asteraceae, Pinaceae, Cupressaceae, and Fabaceae. Phanerophytes represent the dominant growth form, and the chorological spectrum is composed mainly of Asian and American taxa. Taxa from subtropical and tropical biomes also showed a significant presence. This study highlighted the clear prevalence in the Basilicata ornamental flora of alien taxa (approximately 80%, of which 21% are naturalized aliens) compared to native ones, which is a phenomenon that is unfortunately widespread and observed worldwide. Full article

Urban waterfront areas (UWAs), which are essential natural resources and highly perceived public areas in cities, play a crucial role in improving the quality of the urban environment. While numerous studies have delved into the visual perception of urban environments, little attention has been paid to understanding how the visual perception of urban riverfront streets (URSs) differs with various aspects within their unique spatial environment. This study took the Gusu District in Suzhou, China, as a case study, applying deep learning to street-view images to identify urban riverside landscape elements and evaluate their visual attention, aesthetic preference, and distinctiveness through eye-tracking technology and questionnaires. Subsequently, a multidimensional assessment was conducted to analyze how landscape elements influence visual perception in the urban riverfront street. This study concludes that (1) riverfront streets in the Gusu District present balanced visual attention, with high aesthetic preference but limited distinctiveness, and only a few roads in the ancient city score highly for distinctiveness. (2) Greenery, traditional-style buildings, water, and riverfronts positively impact visual perception, while buildings have a negative impact, and backgrounds such as the sky and roads exhibit minimal influence. This study validated the scientific accuracy, appropriateness, and precision of assessments of visual attention, aesthetics, and distinctiveness to quantitatively evaluate the multidimensional human perception of URSs. Full article