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Semantic segmentation of urban street scenes has attracted much attention in the field of autonomous driving, which not only helps vehicles perceive the environment in real time, but also significantly improves the decision-making ability of autonomous driving systems. However, most of the current methods based on Convolutional Neural Network (CNN) mainly use coding the input image to a low resolution and then try to recover the high resolution, which leads to problems such as loss of spatial information, accumulation of errors, and difficulty in dealing with large-scale changes. To address these problems, in this paper, we propose a new semantic segmentation network (HRDLNet) for urban street scene images with high-resolution representation, which improves the accuracy of segmentation by always maintaining a high-resolution representation of the image. Specifically, we propose a feature extraction module (FHR) with high-resolution representation, which efficiently handles multi-scale targets and high-resolution image information by efficiently fusing high-resolution information and multi-scale features. Secondly, we design a multi-scale feature extraction enhancement (MFE) module, which significantly expands the sensory field of the network, thus enhancing the ability to capture correlations between image details and global contextual information. In addition, we introduce a dual-attention mechanism module (CSD), which dynamically adjusts the network to more accurately capture subtle features and rich semantic information in images. We trained and evaluated HRDLNet on the Cityscapes Dataset and the PASCAL VOC 2012 Augmented Dataset, and verified the model's excellent performance in the field of urban streetscape image segmentation. The unique advantages of our proposed HRDLNet in the field of semantic segmentation of urban streetscapes are also verified by comparing it with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the application of information technology under the goal of developing a smart city, smart city streetscape design is emerging and gradually developing. Due to the lack of attention to "smartening" traditional streetscape design, the existing urban streetscape design falls short in meeting the needs of smart city development. Regarding current research, more in-depth research should integrate the streetscape design system into the development of smart cities and build a practical smart-streetscape system. This paper is part of a larger study that aims to explore suitable streetscape sustainable design guidelines within the smart city context by integrating a set of guidelines based on the four dimensions (the city, society, the economy, and the environment) of a sustainable streetscape. Methodologically, this study conducts a systematic literature review of the discussion of streetscapes in the context of smart cities, finding 7 first-level attributes and 39 s-level candidate guidelines for smart city streetscape sustainable design. In the second stage, experts' opinions were collected through a Delphi method, and the attributes and guidelines were screened and revised based on experts' theoretical and practical knowledge. Through three rounds of Delphi method review and Likert scale (5 scores) to score the attributes and guidelines, the finally 7 first-level attributes and 38 s-level guidelines were determined. Use the analytic hierarchy process (AHP) to determine the weightage of guidelines. Finally, it analyzes how the guidelines are relevant to the four dimensions of sustainable streetscape. Subsequently, the guidelines will be used to develop an SCS design framework that will be useful for advancing our understanding of smart city streetscapes promote urban sustainable development and can be used to explore how friendly the smart city streetscapes are to its residents. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article explores citizens' creation and knowledge of the sensory environment in town and city centres in Great Britain during the mid-20th century as a vital part of urban history. It describes the sources used to study the citizen experience in the central streets, including oral histories, autobiographies, surveys and social media. It discusses the emotional responses of citizens to being in close contact with other people in crowds on busy streets.

Purpose: Urban mobility has substantially evolved in several western countries, shifting from interest in road expansion strategies to cater motorized movement to the emphasis on sustainable mobility. This is, however, not the case in several developing countries that still try to accommodate vehicular flows in inner historic cities. This paper aims at providing an assessment framework that helps in evaluating the effect of streetscape development on the walking and cycling environment in historic contexts. Design/methodology/approach: This research follows a two-phase methodology. Phase 1 is the investigation of the literature review including the streetscape design, Sustainable Development Goals (SDGs) and indicators for the assessment of walking and cycling environment. This phase results in developing a set of indicators for the assessment. Phase 2 is the case study including, methods, steps and results of the assessment based on the output of Phase 1. This phase concludes with a discussion on the challenges and recommendations for the enhancement. Findings: The streetscape development in Afrang was insufficient and negatively affected the walking and cycling environment. It was motorized-oriented, instead of enhancing green mobility. The interventions led to more crowds, safety risks and less pleasant experience. Moreover, the car users' experience was enhanced initially; however, the traffic situation did not persist. A sustainable urban mobility approach is necessary to be implemented with consideration to the global level and the relation to SDGs. Originality/value: There is a gap in tackling the research problem both within the context of Port Said in particular and Egyptian context in general. Local authorities need a clear structured methodology to follow in the development of the streetscape. The assessment indicators gathered can be the basis for evaluating future plans. [ABSTRACT FROM AUTHOR]

It is known that street elements influence the vernacularity and livability of urban spaces, the quality of life and the vitality of society everywhere in the world. Thus, there has been an urgent need to examine the quality and suitability of streets, specifically the commercial streets facing rapid developments and urbanization. Indeed, it is more so in Jordan. In this context, this research investigates the effect of physical characteristics of elements of livable streets where people can enjoy life and act freely. In other words, produce vernacularity. The research employs mixed methods; qualitative and quantitative methods within a case study. Data collection techniques include a desk study, direct observation and a questionnaire survey. The findings indicate that applying and paying attention to the characteristics such as color, shape, size, placement of the signs, colors of light, and light in general can achieve livable streets that promote vernacularity. The study suggests a framework to regulate the priorities of principles and the variables of streetscape elements, to help the decision-makers in municipalities and urban planners to bring about livable streets: in other words, promote vernacularity in cities. [ABSTRACT FROM AUTHOR]

Political geographers and sociologists working in the field of critical toponymies have demonstrated that renaming the streetscape follows invariably after a regime change. Scholars have barely gone beyond documenting the extent of toponymic change at the level of particular places, however, usually the capital cities of countries from the former socialist bloc and other postdictatorial societies. This article sets out to address toponymic changes at the country level, by examining the complete national street nomenclature in postsocialist urban Romania. For this purpose, a data set comprising the entire collection of urban street names in Romania, together with all the street name changes that occurred during postsocialism, was constructed from multiple sources (N = 37,076). A series of multilevel logistic regression analyses were performed to model statistically the effects of various street- and locality-level variables on postsocialist street renaming. The results of these multilevel logistic regression analyses indicate that toponymic revision after the fall of state socialism is shaped by the intersection of street-level properties (e.g., artery class and features regarding the street name itself) and locality-level characteristics (e.g., the historicity of urban status and the ethnopolitics played out at the level of each city and town). The article is the first to analyze the shifting political geography of urban nomenclatures at a national level based on a complete data set of street names. The analytical model advanced in this article, based on postsocialist Romania, could be used to inform similar research on other geographical settings and historical contexts. [ABSTRACT FROM AUTHOR]

Environmental justice research largely focuses on the allocation of public resources in terms of quantified distribution and accessibility. This study suggests that the quality of landscape equity, an important urban resource, should be evaluated as it typically appears as a combination of elements. This brings another perspective to the attention of landscape equity in addition to the provision equity: the equity in the quality and level of provision. From this perspective, this study explores the equitable role of streetscape qualities in promoting human physical, mental, and social health. Using street view images, questionnaires, artificial intelligence-based image analysis, geographic information system-based cluster analysis, and correlational analysis, the spatial distribution of health promotion streetscapes and health-influential streetscape elements were presented and compared within the scope of the Shanghai Outer Ring Road. The results disclosed an uneven distribution of streetscapes that can promote human health within research sites. Streets with poor health ratings are concentrated in the Huangpu, Hongkou, and Yangpu districts, primarily in the western part of Shanghai around Shanghai Railway Station, People's Square, and Yuyuan Garden, where the old city centre is located. Compared with the distribution of health-influential elements, less healthy streets are more likely to be accompanied by low visibility of 'vegetation' and a high proportion of 'buildings'. Streetscape design implications were then discussed in terms of providing people with equal health opportunities. Research outcomes inspired an essential quality perspective in health equity research, and it can be especially helpful when the provision and accessibility can hardly be moderated in high-density cities. [ABSTRACT FROM AUTHOR]

Factors that influence women's perceptions of safety, and how these factors are spatially distributed in Canadian cities, are understudied. This case study determined key factors that affect perception of safety in Halifax, Nova Scotia. A survey asked participants to choose from a list of factors identified in empirical literature those that most positively or negatively affected their perceptions of safety. Media portrayal and stories from friends was a significant negative factor on perceptions of safety; presence of people on the street was the most important positive factor. A weighted multi-criteria analysis (MCA) created a proxy for levels of perceived safety across streetscapes, showing which streets are most likely to be perceived as safe or unsafe by women. Findings suggest that women feel safer when the number of people on the street is increased, which can be achieved through mixed-use areas. [ABSTRACT FROM AUTHOR]

The streetscapes of old urban districts record the changes in urban space and the vitality of socio-economic entities like storefronts. However, prior studies of urban vitality have preferred the demand end of crowd agglomeration to the supply end of commercial businesses, while the refined application of street-view images (SVIs) and the spatial heterogeneity resulting from sectional differences among elements deserve further research. Under this context, this paper took both the alive and the closed storefronts as the objects and developed an analytical framework based on machine learning and SVIs to analyze the characteristics of the streetscape and the economic vitality, followed by a regression analysis between them with a multiscale geographically weighted regression (MGWR) model. Our findings comprise three aspects: (1) despite the sum of the storefronts being more often used, combining the alive and the closed businesses is beneficial to reflect the real economic vitality; (2) as a reflection of the spatial heterogeneity and sectional differences of elements, the asymmetric streetscape has a significant influence on the economic vitality; and (3) although different factors from the streetscape can influence economic vitality differently, based on varied proxies of the vitality, three factors, namely, higher difference value of the signboards, higher sum of glass interfaces, and lower difference value of the glass interfaces, can benefit the economic vitality. This research can support urban physical examination and the regeneration of old urban districts for urban planners, designers, and decision-makers, and provide new perspectives and proxies as well as a more fine-grained analysis among the traditional studies on economic vitality. [ABSTRACT FROM AUTHOR]

The significance of urban streets lies in their role as the fundamental structure and framework of a city. Streets acquire extraordinary power as they endure the passage of time, accumulating special meanings and memories. This inherent strength is so influential that proper organization and utilization of existing streets can render the construction of new ones unnecessary. Valiasr, spanning 18 kilometers, stands as the longest street in Tehran and the Middle East. Beginning at the railway square in the south of Tehran at an altitude of 1100 meters above sea level, it extends to Tajrish square in the north, reaching an altitude of 1600 meters above sea level. The street boasts wide sidewalks, a sizable pond, and a line of ancient plane trees, enhancing its aesthetic appeal. Originating during the Qajar era (1785-1925), various segments of Valiasr were constructed to link the capital with city gates. In the early Pahlavi period (1925-1979), it was further developed to connect summer palaces to the city. The street's design, implemented without strict adherence to existing buildings, aimed to establish a direct connection between the north and south of Tehran. During this era, significant government and public structures, royal residences, and palaces were constructed along both sides of Valiasr in adherence to established construction regulations from 1928. This period also witnessed the construction of cultural buildings, public parks, gardens, universities, and medical centers. Following the Islamic Revolution of Iran (1979), Valiasr Street evolved into one of Tehran's traditional shopping centers and promenades, reflecting the city's dynamic cultural shifts. Each historical period of Iran has profoundly impacted Valiasr Street, shaping it into a symbol of Tehran's historical and cultural evolution. To delve into the changing trends and meanings associated with Valiasr Street from 1925 to the present, a comprehensive approach was adopted. A total of 100 street users, encompassing residents, pedestrians, traders, and various stakeholders, were interviewed on-site within the study area. Subsequent to these interviews, a meticulous analysis of the gathered data was conducted, facilitating the synthesis and preparation of a comprehensive diagram. The findings reveal that a multitude of factors, including functional, economic, social, political, and cultural elements, have contributed to the evolving mental imagery associated with Valiasr Street across distinct epochs. In its early existence until 1925, the perceptual influence of the observer led to Valiasr Street being conceived as a narrow garden alley nestled among the Qajar city gardens. Transitioning into the period between 1925 and 1979, the passage of time transformed its role into a communication route connecting newly constructed palaces. Moreover, the increasing influence of temporal dynamics, coupled with cultural and social components, elevated Valiasr Street into the consciousness of the populace, turning it into a cherished urban space among the residents of Tehran. The period from 1979 until the present marks a notable phase in the urban life of Valiasr Street. Following a period of stagnation, the street has undergone a revitalization, and the impact of all four meaning-forming components, with a particular emphasis on the temporal aspect, has led to its designation as a cultural heritage site of the city of Tehran in 2011. This recognition culminated in its inclusion in the list of national heritage sites. [ABSTRACT FROM AUTHOR]

The architectural façade has been a site of intensive experimentation and innovation throughout the 20th century, something that continues to this day, resulting in a vast range of architectural imagery, often incohesive in the post-modern reality. This research explores contemporary façade types and classifies the character of exterior building surfaces. In this paper, we aim to explore how the façade has been designed and has affected its surroundings. How and why has the façade evolved in the ways that it has? Is it the material innovation, structural novelty, the new design techniques or new aesthetics? We adopt a method of analytical induction to extract the most prevalent façade themes from relevant contemporary literature, characterize their meanings and categorize them in order to better explain the many sides of the façade. We set out to define the principles of façade design to then develop a general categorization, which can be applied to most building façades in recent history. [ABSTRACT FROM AUTHOR]

Physical disorder is associated with negative outcomes in economic performance, public health, and social stability, such as the depreciation of property, mental stress, fear, and crime. A limited but growing body of literature considers physical disorder in urban space, especially the topic of identifying physical disorder at a fine scale. There is currently no effective and replicable way of measuring physical disorder at a fine scale for a large area with low cost, however. To fill the gap, this article proposes an approach that takes advantage of the massive volume of street view images as input data for virtual audits and uses a deep learning model to quantitatively measure the physical disorder of urban street spaces. The results of implementing this approach with more than 700,000 streets in Chinese cities—which, to our knowledge, is the first attempt globally to quantify the physical disorder in such large urban areas—validate the effectiveness and efficiency of the approach. Through this large-scale empirical analysis in China, this article makes several theoretical contributions. First, we expand the factors of physical disorder, which were previously neglected in U.S. studies. Second, we find that urban physical disorder presents three typical spatial distributions—scattered, diffused, and linear concentrated patterns—which provide references for revealing the development trends of physical disorder and making spatial interventions. Finally, our regression analysis between physical disorder and street characteristics identified the factors that could affect physical disorder and thus enriched the theoretical underpinnings. [ABSTRACT FROM AUTHOR]

Placemaking is an initiative with roots in the 1960s and 70s to enhance public life in the urban setting. This complex notion requires multiple disciplines, flexibility, and a human-centric approach toward development. While developing new streets and enhancing the existing, built environment, professionals, community residents, and business owners must consider multiple elements to bring people to the street: furniture, vegetation, walkability, art, and culture. With this sidewalk interaction comes the opportunity for economic development. When the people are drawn in, signage can inform the pedestrian. This study builds a collection of 200 photos, representing four levels of placemaking intensities. Each photo is coded into 600 cells to count the quantity of placemaking elements. Visual attentive software (VAS) is then used to measure the sign effectiveness to visually stand out. Placemaking initially impacts preattentive visibility, while the building façade, road surface, tree canopy, landscaping, and the sky each play a role. Planners, designers, and business owners can use these findings to better inform the impact and interaction of streetscape and business. [ABSTRACT FROM AUTHOR]

Evaluating the role of physical elements in creating visually pleasing streetscapes has not been deeply addressed in recent research flow, especially for the context of Kuala Lumpur City. The gap is also accentuated by Kuala Lumpur City Hall as disharmony and inconsistency in streetscapes and lacking visual coherence in most streets in Kuala Lumpur. Therefore, the current paper seeks to evaluate the role of physical and visual elements in creating visually pleasing streetscapes of Bukit Bintang and Tuanku Abdul Rahman using a self-administered questionnaire survey. The respondents of the study comprise 330 passers-by aging from 18 to 50 years old, who visit the streets, reside, or work there. The results demonstrate that transparency, the quality that makes the function of a building visually accessible, and seating places play the most and the least important role in creating streetscapes in the study areas respectively. Comparison of the results in two streets highlights that harmony between and consistency in the facades of modern and traditional buildings, simplicity in design elements of facades, inviting building entrances, and covered walkways contribute to creating attractive streetscapes. Overall, the findings build foundations for restoring the visual coherence, which results in resilient streetscapes in cities. [ABSTRACT FROM AUTHOR]

Owing to the increasing focus on places in urban planning and design, methods to evaluate the quality and value of urban places is crucially needed. Many studies use deep learning models to identify the proportion and composition of landscape elements in images for evaluation. The accuracy of semantic segmentation achieved with such models is often validated using Cityscapes, a street-level image dataset taken from German cities. However, few studies have quantitatively revealed the inference accuracy decrease caused by culture-specific characteristics of streetscapes.In this study, we calculated by-class intersection over union (IoU) and newly-defined indices of false inferences to demonstrate how and to what extent deep learning models can infer each landscape element falsely when applied to Japanese street-level images. Our analysis revealed that certain landscape elements are more difficult to infer correctly based on specific causes, such as their appearances in images and unique characteristics of the fixed physical configuration of Japanese streets. By applying the false inference categorization framework presented in this study, researchers can adjust their approaches considering two aspects: a decrease in inference accuracies of deep learning models and the impact of culture-specific characteristics of streetscapes on people's perception and valuation of urban places. Based on the results and analyses, a future research direction is to develop and implement more accurate image recognition models considering culture-specific characteristics to understand people's perceptions of urban spaces and assess the value of urban places by using the big data including street-level images. [ABSTRACT FROM AUTHOR]

Virtual 3D modeling is widely implemented in urban planning and design. To evaluate urban planning modeling, based on existing computer vision models, this article aims to improve performance in the field of human perception analysis for urban street views. In this study, the PSP module extracts detailed features from recognized objects of different sizes, an attention mechanism is applied to solve the problem of large information differences in pictures, and transfer learning technology is used to expand the model to the field of virtual 3D modeling to extract more representative and universal features, similar to how humans perceive street view information. Finally, we obtain a more objective, stable, and accurate neural network model that imitates human perception. This evaluation model converges within the correct interval on the training and validation datasets compared with an evaluation of virtual modeling by a large number of people. [ABSTRACT FROM AUTHOR]

Increase in the variety of development in urban context has made it more complicated and complex for the users of public spaces. Absence of sufficient information to read the surrounding causes psychological anxiousness leading to perceived danger or discomfort for the urbanites. Consequently, perceived safety and comfort of the users is distinctively low in urban contexts, creating neglected and underused spaces. Complexity is one of the information processing variables as per Kaplan and Kaplan's informational model which helps users to comprehend the surrounding environment. The streetscape plays a vital role in the daily movement patterns within the urban cities and is the transition boundary between the public and private realms. Visual complexity of these streets is a result of different configurations of elements within the urban areas. This research is conducted to ascertain the relationship between visual complexity levels of the streets with the perceived safety and comfort of the users. Shannon Diversity Index (SDI) and Fractal dimension analysis were conducted with 48 SVIs (Street View Images) selected within 1km radius of Colpetty junction, Colombo Sri Lanka covering all the possible compositions found within the context. The visual index data extraction had identified ten major components within the selected 48 SVIs. 78 subjective ranking responses for perceived safety, comfort (preference) and perceived complexity were collected from snowball sampling. Findings of the study revealed that perceived safety levels and preference scores for the SVIs are related to the Shannon Diversity Index calculation in an inverted 'U' shape where the highest and lowest SDI values are related with low preference scores and low safety levels. The SVIs with medium SDI values are perceived as the safest and most preferred by the users of urban streets of Colombo Sri Lanka. The SDI and fractal dimension values were significantly correlated with the perceived complexity scores of the users. The results of this study can be accommodated in the planning and designing of urban streetscapes of tropical climates for sustainable and friendly urban expansions. [ABSTRACT FROM AUTHOR]

Streetscape is an important factor that forms the image of a city. The visual value and preference in streetscape images need to be measured by examining the interaction between places and visitors in the real environment. The study applied a method to recognize and evaluate urban streetscape from the perspective of tour bus passengers, and proved its effectiveness in streetscape visual attraction research. In this study, a visitor-employed photography (VEP) survey was conducted along 32 blocks on the No. 1 urban tour bus (with an average speed of 28 km/h) in Suzhou, China; the visual attributes and significance of urban streetscapes were recognized through empirical research on tour bus visitors' on-site experience. The researcher cultivated the emotional motivations and block distribution captured by 30 respondents through analyzing the types of photos the respondents liked and disliked. The results of this study show that, in general, positive streetscape types tend to be of regional landscape, with cultural and readable emotional motivations. Negative streetscape types tend to be of modern architecture and construction sites, with unreadable and unattractive emotional motivations. The block cluster analysis, based on the average frequency/km of positive and negative streetscape photos, shows that the four block unit clusters were perceived by visitors with deviation between the land use image and the planned land use. The bus VEP method revealed that the visual attractiveness and preferences of streetscape were highly context-specific, rather than for performance. [ABSTRACT FROM AUTHOR]

Building appearances profoundly shape the urban visual landscape, influencing city images and the quality of urban life. Traditional methods for evaluating the perceptual and aesthetic qualities of building facades are often limited in scope. Despite recent studies that have sought to understand human perception of urban streetscapes, our grasp of how individuals perceive building exteriors on a broader scale and the subsequent impact on holistic street experiences, remains largely unexplored. In this study, we integrate a traditional survey-based evaluation framework with machine learning techniques to analyse human perception of over 250,000 building images from Singapore, San Francisco, and Amsterdam. Specifically, deep learning models trained on crowdsourced ratings of 1200 building images across six perceptual attributes — complex, original, ordered, pleasing, boring, and style — achieve over 72% accuracy. This novel approach enables adaptive and comparative analyses of building appearances across regions, revealing spatial patterns in the perception of architectural exteriors and their relationships with functions, age, and location. Moreover, by applying propensity score matching to match images based on their features, we mark one of the first efforts to investigate the perceptual impacts of buildings on streetscape perceptions. The results show that streetscapes with higher levels of complex, pleasing, and historical ambience from buildings elicit more positive perceptions, whereas modern and monotonous exteriors often evoke holistic feelings of being "boring" and "depressing". These findings offer architects and city planners valuable insights into public sentiment towards city-level building exteriors and their influence on urban identity and perception. • A deep learning approach is developed to evaluate building exteriors in three cities. • Spatial patterns of architectural design are identified from street view imagery. • Building characteristics in cities are analysed using objective building attributes. • The influence of building perceptions on overall streetscape perceptions is quantified. • The historical and complex ambience of buildings enhance streetscape perception quality. [ABSTRACT FROM AUTHOR]

The acceleration of urban imagery data analysis, driven by computer vision (CV), has created noteworthy opportunities for urban studies and planning. Data on street environments with high granularity derived from geo-tagged street views allow urban researchers to obtain geospatial data on greenery, pavement materials, and dimensions, building facades, urban furniture, lighting, vehicle presence, etc. However, how such attributes have been classified and used to address urban studies, planning, or mobility questions remains relatively poorly understood among non-technical researchers. Targeting urban planning and design researchers who do not have a background in CV, this paper reviews planning-relevant attributes that CV approaches of urban streetscapes have delivered to date and examines some of their research applications. We present a systematic analysis of 146 papers scrutinizing 104 street attributes in four groups. By exploring a subcollection of 24 papers, we discuss the effectiveness of those attributes being incorporated into current quantitative urban studies. This study's primary contribution lies in providing a comprehensive summary of CV-driven street attributes, their applications, and the algorithms used, serving as a valuable resource for future urban research. Additionally, we identify key challenges in this field, such as unclear definitions of attributes, a disproportionate emphasis on selecting models and features, and the absence of standardized measurement and definition methods. Furthermore, we offer recommendations for future research directions in this area. • We review 104 street attributes collected with computer vision in urban studies • We summarize 23 chosen attributes' correlations with known urban design measures • We provide a table of CV-driven street attributes with model details, references, and impacts • We highlight challenges in attribute definitions, research focus, and impact clarity • We provide recommendations for standardizing data and future research [ABSTRACT FROM AUTHOR]

• Measured 22 subjective perception scores from street-view images. • Model considers the relationships among all subjective perception evaluation items. • Modeled subjective perceptions with high accuracy and low computation cost. • The 22 subjective perceptions were classified into four classes (attractive, old-fashioned, calm, and lively) • Identified landscape elements that affect each subjective perception class. Developing a model to evaluate urban streetscapes based on subjective perceptions is important for quantitative understanding. However, previous studies have only considered limited types of subjective perceptions, neglecting the relationships between them. Further, accurately measuring subjective perception with low computational costs for large-scale urban regions at high spatial resolutions has been difficult. We present a deep-learning-based multilabel classification model that can measure 22 subjective perceptions scores from street-view images. This model uses the results of a web questionnaire survey encompassing 22 subjective perceptions, with 8.8 million responses. Our model demonstrates high accuracy (0.80–0.91) in measuring subjective perception scores from street-view images and achieves low computational cost by training on 22 subjective perception relationships. The 22 subjective perceptions were analyzed using PCA and k-means analysis. By categorizing the 22 subjective perceptions into a two-dimensional space visualized and grouped into distinct groups—positive, negative, calm, and lively—we unearthed vital insights into the intricate nuances of human perception. In addition, the study used semantic segmentation to extract landscape elements from street-view images and applied ℓ1-regularized sparse modeling to identify the landscape elements structurally correlating with each subjective perception class. The analysis revealed that only seven out of nineteen landscape elements significantly correlated with subjective impressions, and these effects varied by class. Notably, sky coverage positively influences positive subjective perceptions, such as attractiveness and calmness, but negatively affects lively impressions. The proposed model can be used to map the overall image of a city and identify landscape design issues in community development design. [ABSTRACT FROM AUTHOR]

Object detection is a central task in computer vision, with applications ranging across the process of smart city construction, city managers always spend a lot of energy and money cleaning street garbage due to the random appearances of street garbage, As deep network solutions become deeper and more complex, they are often limited by the amount of training data available. With this in mind, to spur advances in analyzing and understanding images, Open CV or Google AI has publicly released the Open Images dataset. Open Images follows the tradition of PASCAL VOC, Image Net and COCO, now at an unprecedented scale. In this project we to implement the Consequently, visual street cleanliness assessment is particularly important. However, existing assessment approaches have some clear disadvantages, such as the collection of street garbage information is not automated, and street cleanliness information is not real-time best performing algorithm for automatically detecting objects. Finally, the results are incorporated into the street cleanliness calculation framework to ultimately visualize street cleanliness levels, which provides convenience for city managers to arrange clean-up personnel effectively. [ABSTRACT FROM AUTHOR]

In the second half of the twentieth century urban landscapes were affected by heavy infrastructural development. With regard to streets, managing the growing fluidity and speed of motor vehicle flows has been a priority for most designers. Nowadays there are multiple driving forces behind a transition that could accommodate different uses within the streets, primarily the cultural, social and economic exchanges that streets faciliated in the past but that have been lost over time. In the spreading practice called "shared street" most signage and traffic lights can be removed to permit the self-regulated and spontaneous circulation of all users and vehicles. This paradigm is discussed with regard to its potential to strengthen urban landscape identity, ensuring accessibility, redefining uses and practices within the street, reducing injuries and misbehaviour, and offering real and perceived safety to all users. "Back to the street" is an ongoing design research project dealing with integration strategies for different kinds of urban streetscapes. Which design features are necessary to encourage a change in attitude, speed and hierarchy for street users? How can these features positively affect urban landscapes in general and the liveability of streets first and foremost? The research attempts to answer these questions by considering street design as a means of triggering the effective enhancement of urban public spaces. The paper is based on the case study of Via San Paolo in Prato (Tuscany) and presents a set of quality requirements for street design such as plant integration, water drainage, and comfortable paths for both cyclists and pedestrians. As the design for separate flows cannot fulfill all the quality features in Via San Paolo, mostly due to its varying width, it is assumed that the "shared street" can replace it in order to enhance community life within the streets while promoting local sustainable mobility. The research examines two options: sharing the street in narrow stretches or along its whole length. A preliminary comparison is proposed to discuss the earlier research outcomes. [ABSTRACT FROM AUTHOR]

• A simplified model based on two indicators: sky view factor and sun coverage factor. • A model between the two indicators with simulated solar energy yield was established. • A validation study showed that the average relative error of the model was 6.6%. • The model reduced the computation time while preserving accuracy. The evaluation of rooftop solar energy potential in cities has a fundamental role in the development and utilization of solar energy. The irradiance-based approach of evaluation is a repetitive calculation process combined with the complex geometry of urban forms. This research paper proposes a framework that utilizes urban streetscapes to quickly estimate the urban solar energy potential using a simplified solar energy yield model based on two indicators: sky view factor (SVF) and sun coverage factor (SCF). First, 327 Google Street View images were converted into fisheye images, which were semantically segmented based on the deep-learning Full Convolutional Network (FCN). Then, the SVF and SCF were calculated based on the method of pixel statistics. Finally, a model with the simulated solar energy yield was established. Furthermore, a validation study was conducted using rooftop solar production data, which showed that the maximum estimation error of the proposed model was less than 8% and the average relative error was 6.6%. Generally, the proposed model reduced the required computation time while preserving a satisfactory degree of accuracy. [ABSTRACT FROM AUTHOR]

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