Search Results (150)

Over the past 30 years, the City of Portland, Oregon, USA, has emerged as a national leader in green stormwater infrastructure (GSI). The initial impetus for implementing sustainable stormwater infrastructure in Portland stemmed from concerns about flooding and water quality in the city’s two major rivers, the Columbia and the Willamette. Heavy rainfall often led to combined sewer overflows, significantly polluting these waterways. A partial solution was the construction of “The Big Pipe” project, a large-scale stormwater containment system designed to filter and regulate overflow. However, Portland has taken a more comprehensive and long-term approach by integrating sustainable stormwater management into urban planning. Over the past three decades, the city has successfully implemented GSI to mitigate these challenges. Low-impact development strategies, such as bioswales, green streets, and permeable surfaces, have been widely adopted in streetscapes, pathways, and parking areas, enhancing both environmental resilience and urban livability. This perspective highlights the history of the implementation of Portland’s GSI programs, current design and performance standards, and challenges and lessons learned throughout Portland’s recent history. Innovative approaches to managing runoff have not only improved stormwater control but also enhanced green spaces and contributed to the city’s overall climate resilience while addressing economic well-being and social equity. Portland’s success is a result of strong policy support, effective integration of green and gray infrastructure, and active community involvement. As climate change intensifies, cities need holistic, adaptive, and community-centered approaches to urban stormwater management. Portland’s experience offers valuable insights for cities seeking to expand their GSI amid growing concerns about climate resilience, equity, and aging infrastructure. Full article

The use of multimodal data can effectively compensate for the lack of temporal resolution in streetscape imagery-based studies and achieve hourly refinement in the study of street walkability dynamics. Exploring the 24 h dynamic pattern of urban street walkability and its diurnal variation characteristics is a crucial step in understanding and responding to the accelerated urban metabolism. Aiming at the shortcomings of existing studies, which are mostly limited to static assessment or only at coarse time scales, this study integrates multimodal data such as streetscape images, remote sensing images of nighttime lights, and text-described crowd activity information and introduces a novel approach to enhance the simulation of pedestrian perception through a visual–textual multimodal deep learning model. A baseline model for dynamic assessment of walkability with street as a spatial unit and hour as a time granularity is generated. In order to deeply explore the dynamic regulation mechanism of street walkability under the influence of diurnal shift, the 24 h dynamic score of walkability is calculated, and the quantification system of walkability diurnal change characteristics is further proposed. The results of spatio-temporal cluster analysis and quantitative calculations show that the intensity of economic activities and pedestrian experience significantly shape the diurnal pattern of walkability, e.g., urban high-energy areas (e.g., along the riverside) show unique nocturnal activity characteristics and abnormal recovery speeds during the dawn transition. This study fills the gap in the study of hourly street dynamics at the micro-scale, and its multimodal assessment framework and dynamic quantitative index system provide important references for future urban spatial dynamics planning. Full article

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

The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. Taking Yubei Village in the Nanxi River Basin as an example, this study combined remote sensing images, real-time drone mapping, GIS (geographic information system), and space syntax, extracted 12 key indicators from five dimensions (landform and water features (environment), boundary morphology, spatial structure, street scale, and building scale), and quantitatively “decoded” the spatial genes of the settlement. The results showed that (1) the settlement is a “three mountains and one water” pattern, with cultivated land accounting for 37.4% and forest land accounting for 34.3% of the area within the 500 m buffer zone, while the landscape spatial diversity index (LSDI) is 0.708. (2) The boundary morphology is compact and agglomerated, and locally complex but overall orderly, with an aspect ratio of 1.04, a comprehensive morphological index of 1.53, and a comprehensive fractal dimension of 1.31. (3) The settlement is a “clan core–radial lane” network: the global integration degree of the axis to the holy hall is the highest (0.707), and the local integration degree R3 peak of the six-room ancestral hall reaches 2.255. Most lane widths are concentrated between 1.2 and 2.8 m, and the eaves are mostly higher than 4 m, forming a typical “narrow lanes and high houses” water town streetscape. (4) The architectural style is a combination of black bricks and gray tiles, gable roofs and horsehead walls, and “I”-shaped planes (63.95%). This study ultimately constructed a settlement space gene map and digital library, providing a replicable quantitative process for the diagnosis of Jiangnan water town settlements and heritage protection planning. Full article

The challenges of globalization and urbanization increasingly impact the Historic Urban Landscape (HUL), yet fine-grained and quantitative methods for evaluating HUL remain limited. Adopting a human-centered perspective, this study introduces a novel framework to quantitatively evaluate HUL through the lens of Historical Appearance Integrity (HAI). An evaluation system comprising four key dimensions (building materials, building colors, decorative details, and streetscape morphology) was constructed using the Analytic Hierarchy Process (AHP). An Elo rating system was subsequently applied to quantify the scores of the indicators. A prediction model was developed based on transfer learning and feature fusion to estimate the scores of the indicators. The model achieved accuracies above 93% and loss values below 0.2 for all four indicators. The framework was applied to the Inner Qinhuai Historical Character Area in Nanjing for validation. Results show that the spatial distribution of HAI in the area exhibits significant spatial heterogeneity. On a 0–100 scale, the average HAI scores were 23.17 for primary roads, 27.73 for secondary roads, and 46.93 for branch roads. This study offers a fine-grained, automated approach to evaluate HAI along urban streets and provides a quantitative reference for heritage conservation and urban renewal strategies. Full article

Understanding how people perceive urban streetscapes is essential for enhancing the visual quality of the urban environment and optimizing street space design. While perceptions are shaped by the interplay of multiple visual elements, existing studies often isolate single semantic features, overlooking their combinations. This study proposes a Landscape Element Combination Extraction Method (SLECEM), which integrates the UniSal saliency detection model and semantic segmentation to identify landscape combinations that play a dominant role in human perceptions of streetscapes. Using street view images (SVIs) from the central area of Futian District, Shenzhen, China, we further construct a multi-dimensional feature–perception coupling analysis framework. The key findings are as follows: 1. Both low-level visual features (e.g., color, contrast, fractal dimension) and high-level semantic features (e.g., tree, sky, and building proportions) significantly influence streetscape perceptions, with strong nonlinear effects from the latter. 2. K-Means clustering of salient landscape element combinations reveals six distinct streetscape types and perception patterns. 3. Combinations of landscape features better reflect holistic human perception than single variables. 4. Tailored urban design strategies are proposed for different streetscape perception goals (e.g., beauty, safety, and liveliness). Overall, this study deepens the understanding of streetscape perception mechanisms and proposes a highly operational quantitative framework, offering systematic theoretical guidance and methodological tools to enhance the responsiveness and sustainability of urban streetscapes. Full article

Urban vitality is a critical indicator of urban development quality and livability. However, existing studies often rely on single-source data or subjective evaluation methods, making it challenging to comprehensively and objectively capture the spatial-temporal characteristics of urban vitality. This study takes Baiyun District in Guangzhou as a case study, integrating multiple data sources—including Points of Interest (POI) data, streetscape elements, transportation networks, land use data, and Baidu heat maps—to construct an urban vitality index and explore its key influencing factors. The results reveal the spatial distribution patterns of urban vitality and the varying significance of different determinants, providing data-driven insights and policy implications for urban planning and development. Full article

Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models to process street view data in urban areas, but this is usually inefficient and inaccurate. The main reason is that they are not applicable to the variable climate of urban scenarios, especially performing poorly in adverse weather conditions such as heavy fog that are common in cities. Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). In the UGSAM, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions. Moreover, we have designed a micro-correction network SCP-Net suitable for specific urban scenarios to further improve the accuracy of urban vegetation positioning. The UGSAM was compared with three classic deep learning algorithms and the SAM. Experimental results show that under adverse weather conditions, the UGSAM performs best in OA (0.8615), mIoU (0.8490), recall (0.9345), and precision (0.9027), surpassing the baseline model FCN (OA improvement 28.19%) and PointNet++ (OA improvement 30.02%). Compared with the SAM, the UGSAM improves the segmentation accuracy by 16.29% under adverse weather conditions and by 1.03% under good weather conditions. This method is expected to play a key role in the analysis of urban green spaces under adverse weather conditions and provide innovative insights for urban development. Full article

A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is difficult due to (1) the coarse scale of common remote sensing data, which do not observe the key environments beneath urban tree canopies, and, (2) conversely, the immense labor of intense, location-specific, ground-based survey campaigns. This work tested whether remotely sensed urban heat merged with land cover heterogeneity and shade/sun fractions, if combined at a sufficiently fine scale so as to be linearly additive, would enable simple and accurate statistical modeling of street-scale urban air temperatures with minimal empirical fitting. We used ground-based thermography of a sample of 12 residential streetscapes in Portland, Oregon, to characterize the land surface temperatures (LST_g) of eleven common urban surface cover types when sun-exposed and in shade. Surfaces were cooler in shade than sun, but with surface-specific differences not explained by greenery nor (im)perviousness. Also, surfaces on streetscapes with more canopy cover, even when sun-exposed at midday, remained significantly cooler than comparable sun-exposed surfaces on streets with less canopy cover, indicating the key significance of partial diurnal shading, not typically accounted for in urban thermal statistical models. We used high-resolution orthoimagery to quantify the area of each surface cover type within each streetscape and computed an area-weighted average surface temperature (T_s), accounting for sun/shade heterogeneity. The data revealed a significant, nearly 1:1 relationship between calculated T_s values and sun-shielded air temperatures (T_a). In contrast, relationships of T_a to tree coverage, impervious area, or the LST_g of dominant surface cover types were all statistically insignificant. These results suggest that statistical models may more reliably bridge the gap between remote sensing urban surface temperatures and reliable predictions of street-scale air temperatures if (1) analysis is at a sufficiently high resolution (e.g., <10 m) to avoid some of the known scale-dependence of urban thermal environments and enable simple weighted linear models, and (2) distinctions between thermal contributions of sunlit and shaded surfaces are included along with the influence of diurnal shading. Such models may provide effective and low-cost predictions of local UHIs and help inform effective street-level approaches to mitigating urban heat. Full article

The development of contemporary commercial districts has profoundly influenced the high-quality growth of urban space, social vitality, and public well-being. Based on the SOR (stimulus–organism–response) theoretical model, this study integrates methods including space syntax analysis, POI diversity measurement, streetscape semantic segmentation, and kernel density estimation. It treats spatial morphology variables of commercial districts as stimulus factors, individual behavioral and perceptual responses as the organism, and commercial vitality as the final response, thereby constructing a mechanism model for understanding vitality in urban commercial areas. Using three representative commercial districts in China as case studies, this research conducts a multi-source data integration analysis. The findings reveal that cultural and historical significance, spatial openness, and the density of functional formats exert significant positive impacts on people’s behavior, perception, and sense of comfort. Other variable factors also contribute to varying degrees. The results provide both theoretical insights and practical guidance for the renewal and design optimization of commercial districts. Future studies are encouraged to expand the spatial and temporal scope of analysis and incorporate dynamic behavioral data to more comprehensively uncover the mechanisms driving the evolution of commercial district vitality. Full article

Urban landscapes have become widespread as urban areas have grown. Studying the urban environment in terms of the ecosystem services provided is a key trend in contemporary science. This article aims to examine selected popular typologies of residential streetscapes in terms of their tree species diversity and the health of their greenery. The method combined an on-site tree inventory and selected indices relevant to the species richness, diversity, evenness, and nativity. Their correlation with the Vegetation Indices (VIs), expressing the health of the greenery and its density, was assessed. The main findings included the identification of positive correlations between the mean VI values and the diversity and evenness indices and a negative correlation with the tree nativity. The diversity and evenness indices could be used to inform landscape planning decisions and to evaluate both existing and projected treescapes. The nativity of trees should not be prioritized during planting selection; rather, the soil and climate conditions should be considered. As a result of this study, a comprehensive framework for assessing the greenness of streetscapes was developed. Its implementation will aid in directing greenery planning in residential areas towards sustainable development and regenerative urbanism. Full article

Driven by the United Nations’ Sustainable Development Goal (SDG 11), the construction of high-quality livable cities has emerged as a central issue on the global agenda. However, existing research primarily focuses on optimizing physical functions, neglecting the dynamic hierarchical nature and emotional experiences of residents’ needs. This study, employing Guangzhou’s Tianhe District as an empirical case, proposes an innovative framework that integrates Maslow’s Hierarchy of Needs theory, the Method of Empathy-Based Stories (MEBS), and deep learning technology for the first time. It constructs a dynamic assessment model of “needs-streetscape elements-spatial quality”, systematically analyzing the livability characteristics and driving mechanisms of high-density urban streets. Tianhe District’s street spaces exhibit the common issue of “functional-experiential imbalance” faced by high-density cities. Furthermore, different streetscape elements in the city demonstrate significant variability in satisfying different hierarchical demand dimensions, with strong sequential relationships among these hierarchies. Adjusting and optimizing the relationships between elements can result in the creation of higher-quality street spaces that meet higher-level needs. The research findings provide differentiated renewal pathways for tropical high-density cities, offer methodological support for global urban governance under the SDG 11 objectives, and indicate directions for improving street quality in urban regeneration practices. Full article

The landscape visual sensitivity (LVS) assessment is recognized as a critical tool for identifying areas most sensitive to landscape changes and for informing multi-resource optimization and allocation strategies. However, conventional large-scale LVS assessment criteria and methodologies developed for natural landscapes do not satisfy the precision-oriented assessment requirements of streetscape visual sensitivity (SVS) in historic districts, nor do they facilitate the operational linkage between assessment outcomes and planning applications. This study proposes an innovative SVS–PAP assessment methodology, which is a systematic integration of the SVS assessment and public esthetic perception (PAP) evaluation. The SVS assessment criteria framework was first improved through the integration of enriched multi-modal datasets. Subjective weights were obtained via the analytic hierarchy process (AHP), incorporating expert and public judgments, while objective weights were derived through the entropy weight method (EWM) based on data information entropy. The integration of both approaches enhances the methodological rigor and scientific validity of SVS weight determination. An SVS–PAP analytical matrix was subsequently constructed through integration of SVS assessments and PAP-based scenic beauty estimation (SBE), enabling the derivation of planning strategies. An empirical validation conducted in Anshandao Historic District yielded four key findings: (1) The SVS–PAP methodology, which integrates subjective–objective evaluation factors and incorporates broad public participation, demonstrates strong scientific validity and reliability, establishing a novel paradigm for SVS assessment and strategic planning; (2) The technical framework—leveraging multi-modal data and GIS spatial analysis techniques—improves assessment precision, operability, and replicability; (3) The planning and management strategies formulated by the SVS–PAP analytical matrix were verified as reasonable, demonstrating effective planning-transition capability; (4) Notably, historical and cultural influences showed significantly higher weighting coefficients across assessment criteria compared to non-historic streetscape assessments. Overall, these research results address the persistent undervaluation of the esthetic and spiritual values of historic landscapes in multi-resource value trade-off and decision-making processes, demonstrating both theoretical and practical significance through a systematic methodological advancement. Full article

Since the reform and opening-up policy, the accelerated urbanization rate has triggered extensive construction of new towns, leading to architectural homogenization and environmental quality degradation. As urban development transitions toward a “quality improvement” paradigm, there is an urgent need to synergistically enhance the health performance of human settlements through the optimization of public space environments. The purpose of this study is to explore the impact of the built environment of urban streets on residents’ perceptions. In particular, in the context of rapid urbanization, how to improve the mental health and quality of life of residents by improving the street environment. Changbai Island Street in the Heping District of Shenyang City was selected for the study. Baidu Street View images combined with machine learning were employed to quantify physical characterizations like street plants and buildings. The ‘Place Pulse 2.0’ dataset was utilized to obtain data on residents’ perceptions of streets as beautiful, safe, boring, and lively. Correlation and regression analyses were used to reveal the relationship between physical characteristics such as green visual index, openness, and pedestrians. It was discovered that the green visual index had a positive effect on perceptions of it being beautiful and safe, while openness and building enclosure factors influenced perceptions of it being lively or boring. This study provides empirical data support for urban planning, emphasizing the need to focus on integrating environmental greenery, a sense of spatial enclosure, and traffic mobility in street design. Optimization strategies such as increasing green coverage, controlling building density, optimizing pedestrian space, and enhancing the sense of street enclosure were proposed. The results of the study not only help to understand the relationship between the built environment of streets and residents’ perceptions but also provide a theoretical basis and practical guidance for urban space design. Full article

Streetscapes in old urban areas are not only an important carrier to show regional economies and city style, but also closely correlate to urban residents’ everyday life and the hustle and bustle in which they live. Nevertheless, previous studies have either focused on a few examples with low-throughput surveys or have lacked a specific consideration of spontaneous features in the data-driven explorations. Furthermore, the impact of spontaneous streetscape features on diversified social sensing has rarely been examined. This paper combined the mobile collection of street view images (SVIs) and a machine learning algorithm to calculate eight types of spontaneous streetscape elements and integrated two online platforms (Dianping and Sina Weibo) to map the distribution of economic vitality and social media perception, respectively. Then, through comparing multiple regression models, the impacts of the spontaneous streetscape characteristics on social sensing were revealed. The results include the following two aspects: (1) overall, the spontaneous streetscape features have a certain similarity in the impact on both dimensions of social sensing in Quanzhou, with significant clustering and transitional trends and strong spatial heterogeneity; and (2) specifically, the spontaneous streetscape elements can be divided into three categories, given the differentiated roles of significantly positive, negative, and polarizing impacts on the social sensing results. For example, proper use of open-interface storefronts, ads, and banners is consistent with the common suggestions, while the excessive pursuit of interface diversity and the use of cultural elements may bring an ambiguous effect. This paper provides a transferable analytical framework for mixed and data-driven sensing of streetscape regeneration and can potentially inspire related decisionmakers to adopt a more refined and low-cost approach to enhance urban vitality and sustainability. Full article