Search Results (35)

Street view imagery has become a vital tool for assessing urban street greenery, with the Green View Index (GVI) serving as the predominant metric. However, while GVI effectively quantifies overall greenery, it fails to capture the nuanced, human-scale experience of urban greenery. This study introduces the Front-Facing Green View Index (FFGVI), a metric designed to reflect the perspective of pedestrians traversing urban streets. The FFGVI computation involves three key steps: (1) calculating azimuths for road points, (2) retrieving front-facing street view images, and (3) applying semantic segmentation to identify green pixels in street view imagery. Building on this, this study proposes the Street Canyon Green View Index (SCGVI), a novel approach for identifying boulevards that evoke perceptions of comfort, spaciousness, and aesthetic quality akin to room-like streetscapes. Applying these indices to a case study in Nanjing, China, this study shows that (1) FFGVI exhibited a strong correlation with GVI (R = 0.88), whereas the association between SCGVI and GVI was marginally weaker (R = 0.78). GVI tends to overestimate perceived greenery due to the influence of lateral views dominated by side-facing vegetation; (2) FFGVI provides a more human-centered perspective, mitigating biases introduced by sampling point locations and obstructions such as large vehicles; and (3) SCGVI effectively identifies prominent boulevards that contribute to a positive urban experience. These findings suggest that FFGVI and SCGVI are valuable metrics for informing urban planning, enhancing urban tourism, and supporting greening strategies at the street level. Full article

Historic cities face a dual challenge of preserving cultural authenticity and adapting to modern urbanization, yet existing studies often overlook the multidimensional coupling mechanisms critical for sustainable urban renewal. This research has proposed a replicable framework to balance heritage conservation, ecological restoration, and pedestrian mobility. Focusing on the historic city of Shenyang, this study evaluated spatial dynamics via the Walkability Index (WI), Green View Index (GVI), and Cultural Heritage Index (CHI), and quantified their coupling coordination patterns. Multisource datasets including OpenStreetMap road networks, POIs, and Baidu street-view imagery were integrated. A Coupling Coordination Degree (CCD) model was developed to assess system interactions. Results revealed moderate overall walkability (WI = 42.66) with stark regional disparities, critically low greening (GVI = 10.14%), and polarized heritage distribution (CHI = 18.73) in Shenyang historic city. Tri-system coupling was moderate (CCD = 0.409–0.608), constrained by green-heritage disconnects in key districts. This work could contribute to interdisciplinary discourse by bridging computational modeling with human-centric urban design, providing scalable insights for global historic cities. Full article

This study investigates the relationship between Green View Index (GVI) and street thermal environment in Hangzhou’s main urban area during summer, quantifying urban greenery’s impact on diurnal/nocturnal thermal conditions to inform urban heat island mitigation strategies. Multi-source data (3D morphological metrics, LCZ classifications, mobile measurements) were integrated with deep learning-derived street-level GVI through image analysis. A random forest-multiple regression hybrid model evaluated spatiotemporal variations and GVI impacts across time, street orientation, and urban-rural gradients. Key findings include: (1) Urban street Ta prediction model: Daytime model: R² = 0.54, RMSE = 0.33 °C; Nighttime model: R² = 0.71, RMSE = 0.42 °C. (2) GVI shows significant inverse association with temperature, A 0.1 unit increase in GVI reduced temperatures by 0.124°C during the day and 0.020 °C at night. (3) Orientation effects: North–south streets exhibit strongest cooling (1.85 °C daytime reduction), followed by east–west; northeast–southwest layouts show negligible impact; (4) Canyon geometry: Low-aspect canyons (H/W < 1) enhance cooling efficiency, while high-aspect canyons (H/W > 2) retain nocturnal heat despite daytime cooling; (5) Urban-rural gradient: Cooling peaks in urban-fringe zones (10–15 km daytime, 15–20 km nighttime), contrasting with persistent nocturnal warmth in urban cores (0–5 km); (6) LCZ variability: Daytime cooling intensity peaks in LCZ3, nighttime in LCZ6. These findings offer scientific evidence and empirical support for urban thermal environment optimization strategies in urban planning and landscape design. We recommend dynamic coupling of street orientation, three-dimensional morphological characteristics, and vegetation configuration parameters to formulate differentiated thermal environment design guidelines, enabling precise alignment between mitigation measures and spatial context-specific features. Full article

Despite the widespread use of street view imagery for Green View Index (GVI) analyses, variations in sampling methodologies across studies and the potential impact of these differences on the results, including associated errors, remain largely unexplored. This study aims to investigate the effectiveness of various GVI calculation methods, with a focus on analyzing the impact of sampling point selection and coverage angles on GVI results. Through a systematic review of the extensive relevant literature, we synthesized six predominant sampling methods: the four-quadrant view method, six-quadrant view method, eighteen-quadrant view method, panoramic view method, fisheye view method and pedestrian view method. We further evaluated the strengths and weaknesses of each approach, along with their applicability across different research domains. In addition, to address the limitations of existing methods in specific contexts, we developed a novel sampling technique based on three 120° street view images and experimentally validated its feasibility and accuracy. The results demonstrate the method’s high reliability, making it a valuable tool for acquiring and analyzing street view images. Our findings demonstrate that the choice of sampling method significantly influences GVI calculations, underscoring the necessity for researchers to select the optimal approach based on a specific research context. To mitigate errors arising from initial sampling angles, this study introduces a novel concept, the “Green View Circle”, which enhances the precision and applicability of calculations through the meticulous segmentation of observational angles, particularly in complex urban environments. Full article

Rapid socio-economic development and imbalanced ecosystem conservation have heightened the risk of species extinction, reduced urban climate adaptability, and threatened human health and well-being. Constructing ecological green space networks is an effective strategy for maintaining urban ecological security. However, most studies have primarily addressed biodiversity needs, with limited focus on coordinating street spaces in human settlement planning. This study examines the area within Chengdu’s Third Ring Road, employing the following methodologies: (1) constructing the regional ecological network using Morphological Spatial Pattern Analysis (MSPA), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and circuit theory; (2) analyzing the street green view index (GVI) through machine learning semantic segmentation techniques; and (3) identifying key areas for the coordinated development of urban ecological networks and street green spaces using bivariate spatial correlation analysis. The results showed that (1) Chengdu’s Third Ring Road exhibits high ecological landscape fragmentation, with 41 key ecological sources and 94 corridors identified. Ecological pinch points were located near urban rivers and surrounding woodlands, while ecological barrier points were concentrated in areas with dense buildings and complex transportation networks. (2) Higher street GVI values were observed around university campuses, urban parks, and river-adjacent streets, while lower GVI values were found near commercial areas and transportation hubs. (3) To coordinate the construction of ecological networks and street green spaces, the central area of the First Ring Road and the northwestern region of the Second and Third Ring Roads were identified as priority restoration areas, while the northern, western, and southeastern areas of the Second and Third Ring Roads were designated as priority protection areas. This study adopts a multi-scale spatial perspective to identify priority areas for protection and restoration, aiming to coordinate the construction of urban ecological networks and street green spaces and provide new insights for advancing ecological civilization in high-density urban areas. Full article

The vitality of riverfront districts, as a crucial component of urban livability, is profoundly influenced by human visual perception of the surrounding environment. This study takes the Pearl River in Guangzhou as an example and explores the relationship between the visual perception of the urban riverfront core landscape area and the vitality of Riverfront Road. Employing subjective environment perception prediction methods and analyzing the riverfront landscape pictures captured by the research team, we quantified six essential perceptual dimensions. Furthermore, we evaluated the vitality of Riverfront Road through a four-step process: 1. measuring key visual indices of Riverfront Road, including the green view index (GVI), water view index (WVI), sky view index (SVI), and building view index (BVI); 2. evaluating the proximity of cultural landmarks to Riverfront Road; 3. calculating the convenience of driving, buses, and subways for Riverfront Road with the network analysis method; 4. deriving the vitality value of Riverfront Road through the combination of hotspot data from Baidu. With the application of random forest and result comparisons, we obtained a comprehensive analysis of the correlation between visual perception of the urban riverfront core landscape area and the vitality of Riverfront Road. The results reveal the significant correlation between these two factors and highlight that visual perception of the old city landscape area is superior to that of the new city, although the cultural landmarks and transportation convenience play essential roles in the improvement of vitality in Riverfront Road. It is evident that relying solely on visual design may fail to prominently boost vitality. Overall, spatial design should adopt a multidimensional approach, integrating various factors such as transportation convenience, social interaction venues, cultural activities, etc., to create a cohesive vitality network. Full article

This study proposes a method to analyze urban greenery perceived from street-level viewpoints by combining geographic information systems (GIS) with image segmentation. GIS was utilized for a geospatial statistical analysis to examine anisotropy in the distribution of urban greenery and to spatialize image segmentation data. The result was the Visual Greenery Field (VGF) model, which offers a vector-based representation of greenery visibility and directionality in urban environments. The analysis employed street view images from selected geographic locations to calculate a Green View Index (GVI) and derive visual vectors. Validation confirmed the reliability of the methods, as evidenced by solid correlations between automatic and manual segmentations. The findings indicated that greenery visibility varies across the cardinal directions, highlighting that the GVI’s average value may obscure significant differences in greenery’s distribution. The VGF model complements the GVI by revealing directional coherence in urban greenery experiences. This study emphasizes that while the GVI provides an overall assessment, integrating the VGF model enriches the understanding of perceptions of urban greenery by capturing its complexities and nuances. Full article

The aim of this study is to reveal the effects of multilevel visual characteristics of greenways on thermal perception in hot and humid regions during summer and to explore the potential of visual design to enhance psychological thermal comfort. Data on light (L), color (C), plant richness (PR), space openness (SO), scenic view (SV), thermal sensation (TS), and thermal preference (TP) were collected through questionnaires (n = 546). Computer vision technology was applied to measure the green view index (GVI), sky view index (SVI), paving index (PI), spatial enclosure (SE), and water index (WI). Using the hill climbing algorithm in R to construct a Bayesian network, model validation results indicated prediction accuracies of 0.799 for TS and 0.838 for TP. The results showed that: (1) SE, WI, and SV significantly positively influence TS, while L significantly negatively influences TS (R² = 0.6805, p-value < 0.05); (2) WI, TS, and SV significantly positively influence TP (R² = 0.759, p-value < 0.05). Full article

Urban green spaces play a crucial role in providing social services and enhancing residents’ mental health. It is essential for sustainable urban planning to explore the relationship between urban green spaces and human perceptions, particularly their visual comfort. However, most current research has analyzed green spaces using two-dimensional indicators (remote sensing), which often overlook human visual perceptions. This study combined two-dimensional and three-dimensional methods to evaluate urban green spaces. Additionally, the study employed machine learning to quantify residents’ visual comfort in green-space environments and explored the relationship between green spaces and human visual perceptions. The results indicated that Kitakyushu exhibited a moderate FCV and an extremely low Green View Index (GVI). Yahatanishi-ku was characterized as having the highest visual comfort. Tobata-ku demonstrated the lowest visual comfort. Natural, GVI, openness, enclosure, vegetation diversity, landscape diversity, and NDBI were positively correlated with visual comfort. FCV and ENVI were negatively correlated with visual comfort. Vegetation diversity had the most impact on improving visual comfort. By integrating remote sensing and street-view data, this study introduces a methodology to ensure a more holistic assessment of green spaces. Urban planners could use it to better identify areas with insufficient green space or areas that require improvement in terms of green-space quality. Meanwhile, it could be helpful in providing valuable input for formulating more effective green-space policies and improving overall urban environmental quality. The study provides a scientific foundation for urban planners to improve the planning and construction of healthy and sustainable cities. Full article

Thermal comfort is a key determinant ruling the quality of urban park visits that is mainly evaluated by equivalent meteorological factors and lacks evidence about its relationship with emotional perception. Exposure to green space was believed to be an available approach to increase thermal comfort, but this argument still needs verification to confirm its reliability. In this study, about ~15,000 streetscapes were photographed at stops along sidewalks and evaluated for green view index (GVI) and plant diversity index in five urban parks of Changchun, Northeast China. The faces of visitors were captured to analyze happy, sad, and neutral scores as well as two net positive emotion estimates. Meteorological factors of temperature, relative humidity, and wind velocity were measured at the same time for evaluating thermal comfort using equivalent variables of discomfort index (DI), temperature and humidity index (THI), and cooling power index (CP). At stops with higher GVI, lower temperature (slope: from −0.1058 to −0.0871) and wind velocity (slope: from −0.1273 to −0.0524) were found, as well as higher relative humidity (slope: from 0.0871 to 0.8812), which resulted in positive relationships between GVI and thermal comfort evaluated as DI (R² = 0.3598, p < 0.0001) or CP (R² = 0.3179, p < 0.0001). Sad score was positively correlated with THI (R² = 0.0908, p = 0.0332) and negatively correlated with CP (R² = 0.0929, p = 0.0294). At stops with high GVI, more positive emotions were shown on visitors’ faces (happy minus sad scores, 0.31 ± 0.10). Plant diversity had varied relationships with GVI in parks depending on age. Overall, our study demonstrated that using imagery data extracted from streetscapes can be useful for evaluating thermal comfort. It is recommended to plan a large amount of touchable nature provided by vegetation in urban parks so as to mitigate micro-climates towards a trend with more thermal comfort that evokes more positive emotions. Full article

The visibility of retail frontages is critical for earning profits from spontaneous traffic visits to retail shops located along a street. The urban tree canopy plays a crucial role in enhancing the street-side environment, yet more is not always better when considering the placement of retail shops behind trees with big canopies. Related evidence in the literature is rarely provided, and an unclear relationship has been reported to exist between the number of shops for a specific retail type and the quantified ratio of the canopy shade in a street view. In this study, both big data crawling and deep learning were employed to unravel this relationship for retail shops in Changchun, Northeast China. The entire study area was divided into 6037 grid cells with a side length of ~0.6 km, wherein the number of shops of five retail types (food and beverage, shopping, life services, entertainment, and hotel) were quantified by computer counting their points of interest (POIs). The canopy shade was evaluated using the green view index (GVI) quantified through the ratio of canopy pixels divided by all the pixels in a street view image obtained through an online map API. A neighboring road network was categorized into four classes: class I road density mainly reduced the number of retail shops, and the road densities of classes III and IV accounted for more retail shops. The relationship between the number of retail shops and the GVI could be fitted with positive skewness curves for class II roads, where the critical peak of the GVI was estimated to be about 3.27%. The optimization scheme indicated that more retail shops should be placed along class I and II roads. In conclusion, more retail shops for food and beverage, shopping, and life services should be placed in the landscape neighboring big canopies. Full article

Understanding the determinants of the availability and spatial fairness of street greenery is crucial for improving urban green spaces and addressing green justice concerns. While previous studies have mainly examined factors influencing street greenery from an aerial perspective, there has been limited investigation into determinants at eye level, which more closely aligns with people’s actual encounters with green spaces. To address this, the Green View Index (GVI) and Gini coefficient were used to assess the availability and spatial fairness of street greenery from a pedestrian’s perspective, using Baidu Street View (BSV) images across 49 subdistricts in Changchun City, China. A dataset of 33,786 BSV images from 1877 sites was compiled. Additionally, 21 explanatory factors were collected and divided into three groups: socioeconomic, biogeographic, and landscape patterns. The Boosted Regression Tree (BRT) method was employed to assess the relative influence and marginal effects of these factors on street greenery’s availability and spatial fairness. The results showed that street greenery’s availability and spatial fairness are predominantly influenced by landscape patterns. Specifically, the percentage of landscape and edge density emerged as the most significant factors, exhibiting a threshold effect on the availability and fairness of street greenery. Increasing the proportion and complexity of urban green spaces can efficiently enhance the availability and spatial fairness of street greenery. These findings lay a new foundation for urban green infrastructure management. Full article

This paper takes the central area of Shenzhen as an example to explore the correlation and differences between 2D and 3D green spaces on urban roads during the summer of 2023. By collecting street view image data and using convolutional neural networks for image semantic segmentation, the Green View Index (GVI) was calculated and combined with the Normalized Difference Vegetation Index (NDVI) for analysis. The results show that the road greening levels in Nanshan District, Futian District, and Luohu District of Shenzhen are relatively high, with GVI exceeding 25%. The Pearson correlation coefficient between the 2D and 3D greening data is 0.5818, indicating a moderate correlation. By analyzing four typical greening scenarios (high NDVI and high GVI, high NDVI and low GVI, low NDVI and high GVI, and low NDVI and low GVI), the study found specific reasons for the differences in green data in different dimensions; the analysis revealed that factors such as building height, density, and elevated transportation facilities significantly affect the accuracy of NDVI in urban spaces. The study suggests that in urban greening assessments, the complementarity and differences between street view data and remote sensing data should be comprehensively considered to improve the accuracy and comprehensiveness of the analysis. Full article

Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs. This review navigates from traditional to modern DL-based semantic segmentation models, illuminating the evolution of the urban greenness measures and segmentation tasks for advanced landscape analysis. It also presents the typical performance metrics and explores public datasets for constructing these measures. The results show that accurate (semantic) segmentation is inevitable not only for fine-grained greenness measures but also for the qualitative evaluation of landscape analyses for planning amidst the incomplete explainability of the DL model. Also, the unsupervised domain adaptation (UDA) in aerial images is addressed to overcome the scale changes and lack of labeled data for fine-grained greenness measures. This review contributes to helping researchers understand the recent breakthroughs in DL-based segmentation technology for challenging topics in UGS research. Full article

Urban street greening, a key component of urban green spaces, significantly impacts residents’ physical and mental well-being, contributing substantially to the overall quality and welfare of urban environments. This paper presents a novel framework that integrates street greenery with accessibility, enabling a detailed evaluation of the daily street-level greenery visible to residents. This pioneering approach introduces a new measurement methodology to quantify the quality of urban street greening, providing robust empirical evidence to support its enhancement. This study delves into Nanjing’s five districts, employing advanced image semantic segmentation based on machine learning techniques to segment and extract green vegetation from Baidu Street View (BSV) images. Leveraging spatial syntax, it analyzes street network data sourced from OpenStreetMap (OSM) to quantify the accessibility values of individual streets. Subsequent overlay analyses uncover areas characterized by high accessibility but inadequate street greening, underscoring the pressing need for street greening enhancements in highly accessible zones, thereby providing valuable decision-making support for urban planners. Key findings revealed that (1) the green view index (GVI) of sampled points within the study area ranged from 15.79% to 38.17%, with notably better street greening conditions observed in the Xuanwu District; (2) the Yuhua District exhibited comparatively lower pedestrian and commuting accessibility than the Xuanwu District; and (3) approximately 139.62 km of roads in the study area demonstrated good accessibility but lacked sufficient greenery visibility, necessitating immediate improvements in their green landscapes. This research utilizes the potential of novel data and methodologies, along with their practical applications in planning and design practices. Notably, this study integrates street greenery visibility with accessibility to explore, from a human-centered perspective, the tangible benefits of green landscapes. These insights highlight the opportunity for local governments to advance urban planning and design by implementing more human-centered green space policies, ultimately promoting societal equity. Full article