ABSTRACT Street visual quality improvement plays an important role in urban development. An important direction for street quality research lies in accurately perceiving the spatial quality

of urban streets and exploring the connection with street constituents. This study applies deep learning to extract visual elements from street view images and uses a human-machine

adversarial model to rate them across six dimensions (beautiful, wealthy, safety, lively, depressing, and boring). Through spatial visualization of street quality, overlay analysis with

network accessibility, and multiple linear regression, it examines the correlations between street space quality and its constituent elements. The results indicate that the streets within

elements, such as pathways and building facades. Positive visual perception such as beautiful, wealthy, safety, lively is positively correlated with plants and pedestrians, and negatively

correlated with walls. It is important to address the distinct types of streets in urban planning, including high-quality/accessibility streets found in urban centers,

high-quality/low-accessibility streets at district junctions with sparse networks, low-quality/high-accessibility streets in the southwestern center and Low-quality/accessibility peripheral

areas characterized by outdated buildings. Strategies should prioritize improvements in street function, greening, building interfaces, and pedestrian connectivity. These measures will help

enhance the overall spatial quality and vitality of the area. In summary, the findings provide data support for more precise urban street improvements and offer a reference for

human-centered urban planning research. SIMILAR CONTENT BEING VIEWED BY OTHERS MEASUREMENT OF SPATIAL HETEROGENEITY IN STREET RESTORATIVE PERCEPTIONS AND STREET REFINEMENT DESIGN Article

Open access 03 June 2025 ANALYSIS OF SPATIAL VARIATION OF STREET LANDSCAPE GREENING AND INFLUENCING FACTORS: AN EXAMPLE FROM FUZHOU CITY, CHINA Article Open access 08 December 2023 SPATIAL

PATTERN AND HETEROGENEITY OF GREEN VIEW INDEX IN MOUNTAINOUS CITIES: A CASE STUDY OF YUZHONG DISTRICT, CHONGQING, CHINA Article Open access 12 April 2025 INTRODUCTION Streets are essential

public spaces for urban residents, providing a physical and social environment for daily interactions and recreation1. As vital connectors between people and the city, streets shape

residents’ experiences, influencing mobility, safety, social interaction, and overall urban life. City managers increasingly focus on street design to enhance socio-economic and cultural

activities, aiming to create more sustainable, livable environments2. City managers are paying increasing attention to the design and planning of urban streets as important hubs of

socio-economic and cultural activities. A deep understanding of the intricate relationship between the components of urban streets and human perception is conducive to a harmonious and

sustainable urban environment. Traditionally, planners and designers relied on labor-intensive methods such as on-site surveys or static maps, which provided limited insights into the visual

and spatial qualities of streets. Inadequate technology for large-scale data processing and a lack of comprehensive data sources have hindered the development of scientific and rational

street planning blueprints. The emergence of streetscape imagery has overcome the previous limitations on the availability of data sources for assessing streets3,4. Streetscape images are

electronic maps based on actual landscapes, and these maps provide richly detailed streetscape images with a wide range of coverage containing a large amount of information about city

streets5,6. Therefore, these street view images have the potential to become an important form of supporting data for assessing the visual perception of city streets. Currently, a number of

Internet companies such as Google, Microsoft, Baidu, and Tencent have launched online street view services7,8. In China, Baidu Maps’ online Street View service already covers a large number

of streets in most cities. As a new data source, Street View images have gradually become a research hotspot. A large number of urban studies based on Street View images include 3D city

reconstruction studies9,10, specific scene recognition11, perceptual safety assessment12, alcohol consumption data extraction13, assessment of visual perception of streets14, and other

related areas. Among them, visual perception of streets is the basis for exploring urban planning and quality of life of residents15. Meanwhile, with the advancement in the field of

computers, it is now possible to utilize convolutional neural networks for fast and accurate fine-grained processing of street images. This technique can effectively recognize various

elements such as buildings, roads, pedestrians, and greenery16. Through the combination of deep learning technology and streetscape images, it can provide refined basic data for the study of

street spatial quality, and at the same time, it can quickly process large-scale data, making it technically possible to quickly and accurately measure the visual quality of streets on a

large scale. However, current research still falls short in revealing the quantitative relationship between street components and human subjective perception, lacking both systematic

approaches and scalability. Consequently, an urgent research gap exists regarding how to combine emerging large-scale street view data and deep learning technologies to achieve

high-precision, wide-coverage measurement of street visual quality. Against this backdrop, this study endeavors to bridge the aforementioned gap: on the one hand, advanced methods such as

deep learning and space syntax are employed to comprehensively quantify street visual quality and its constituent elements; on the other hand, subjective perception dimensions are integrated

with objective quantitative metrics to systematically evaluate how the street environment influences residents’ daily lives and urban development. This integration enhances our

understanding of the intricate relationship between street visual characteristics and residents’ experiences, thereby offering more targeted optimization strategies for urban planning and

decision-making. In this study, considering the subjective feelings of people who are the main users of streets and the objective physical characteristics of streets, an efficient evaluation

model of urban street visual quality is constructed by using multi-source big data such as street view images and OSM street vector data, combined with deep learning, spatial syntax and

Random Forest model. It can be used for large-scale and high-detailed evaluation of the visual quality of urban streets to better provide reference for urban planning assessment and assisted

decision-making, as well as the improvement of the quality of the human environment. LITERATURE REVIEW TRADITIONAL STREET QUALITY STUDY The study of street space quality has its roots in

the 1960s, with seminal contributions from urban theorists such as JACOBS J and LEFEBVRE H, who emphasized the human-scale perspective in urban design. Jacobs, in her work _The Death and

Life of Great American Cities_ (1961), critiqued the separation of functions in cities and argued that the vitality of streets is directly linked to their ability to support diverse,

pedestrian-friendly environments17. Lefebvre, in _The Production of Space_ (1974), introduced the idea that the spatial organization of cities is a socially constructed phenomenon,

influencing both daily life and urban interactions18. These early studies laid the groundwork for understanding the importance of human-scale urban design, focusing on the street as a space

of social interaction and pedestrian use. Building on these foundations, Kevin Lynch’s influential book _The Image of the City_ (1960) further explored the way individuals perceive and

experience urban spaces. Lynch’s work emphasized the concept of “imageability” and how streets contribute to the legibility and identity of cities19. His framework categorized urban spaces

into elements such as paths, edges, districts, nodes, and landmarks, providing a systematic approach for evaluating street spaces from a psychological and sensory perspective. As urban

studies progressed, research on street quality expanded to cover various types of urban environments, including commercial districts20, public squares21, and residential streets22. For

example, studies have highlighted the role of pedestrian-friendly streets in enhancing social interaction and contributing to the economic vitality of city centers. In contrast, other

research has examined the impact of traffic congestion, safety, and environmental factors on the perceived quality of streets in residential areas23. These studies have revealed that street

quality is a multifaceted concept, encompassing physical, social, and psychological dimensions. Over the past few decades, there has been a marked shift towards more objective and systematic

methods of street quality evaluation. Traditional approaches, which relied heavily on manual measurement and subjective assessment, often faced challenges in scalability and precision. For

instance, methods such as surveys and visual inspections were limited by researcher biases and logistical constraints, making large-scale and detailed assessments of street quality difficult

to conduct. In response to these limitations, there has been a growing trend toward integrating quantitative techniques into street quality analysis24. One significant milestone in the

development of quantitative methods for street evaluation was the introduction of spatial syntax in 1984 by Bill Hillier and Julienne Hanson25. Spatial syntax focuses on the topological

properties of street networks, enabling researchers to quantify and visualize accessibility, connectivity, and movement patterns within urban spaces. This approach provides valuable insights

into the structural organization of streets, as well as their potential for fostering social interactions and facilitating efficient transportation. Today, spatial syntax remains an

essential tool in urban planning and design, with applications ranging from large-scale city planning to micro-level pedestrian movement analysis26. STREET QUALITY RESEARCH INCORPORATING BIG

DATA The rapid advancement of computer technology has greatly expanded the ways in which urban street quality can be analyzed. The diversification of data collection methods, particularly

with the introduction of Point of Interest (POI) data, has enabled more comprehensive and precise quantitative evaluations of street spaces. POI data, which captures locations of notable

urban features such as shops, public facilities, and cultural landmarks, has proven especially useful in urban studies. Zhang et al. (2019) leveraged POI data and public awareness tasks to

map the accuracy of POI data to the linear space of streets, creating a detailed method for classifying street types based on urban functions and use patterns27. This approach marked a

significant step in fine-grained street quality assessment, allowing for a more nuanced understanding of how different types of streets serve their surrounding communities. Similarly, Liu et

al. (2021) used POI data to analyze the relationship between urban street characteristics and economic activity, showing how street designs influence commercial vitality in urban areas28.

Qin et al. (2020) explored the relationship between street vitality and urban data in tourist cities, combining POI data with Baidu heat maps and OpenStreetMap (OSM) road network data. Their

study offered insights into how different types of urban spaces, such as tourist districts, are shaped by the dynamics of human activity and transportation networks29. Such studies show how

big data can not only support basic spatial assessments but also provide dynamic metrics for understanding the vitality and usage patterns of streets in real time. Additionally, Zhao et al.

(2021) extended this analysis by incorporating weather and traffic data, showing that street vitality in tourist districts is highly sensitive to external factors such as weather conditions

and public transport availability30. As the availability of data sources has grown, quantitative measurement techniques for street quality have become increasingly diverse. Geographic

Information Systems (GIS) remain one of the most widely adopted methods, offering powerful tools for spatial analysis and integration of various datasets, such as road networks, POI, and

demographic data. GIS-based evaluations are commonly employed to assess street accessibility, walkability, and the distribution of urban amenities. Research by Xiao et al. (2021)

demonstrated how GIS-based models, integrating POI and traffic data, can be used to assess the accessibility and pedestrian comfort of city streets31. These approaches, coupled with

demographic data, have allowed for a deeper understanding of how street designs impact the accessibility and inclusivity of urban spaces. Similarly, Wang et al. (2022) applied GIS to assess

the impact of green spaces on urban street quality, finding that areas with higher green coverage tend to exhibit better pedestrian experiences and overall street satisfaction32.

Furthermore, the integration of 3D modeling and virtual reality (VR) technologies into street quality research has provided new possibilities for spatial analysis. In recent years, 3D

modeling techniques have gained popularity for their ability to represent urban streets in high detail. 3D models, particularly those generated using virtual reality (VR) and augmented

reality (AR) technologies, allow for immersive and interactive experiences that aid in evaluating street environments. Studies like those by Zhao et al. (2022) have explored how 3D

simulations can be used to assess not only the visual appeal of streets but also their functionality in terms of pedestrian movement, traffic flow, and environmental conditions33. This

method offers a more comprehensive approach to street evaluation, incorporating both physical and experiential aspects of street quality. Furthermore, Liu and Wang (2023) developed a 3D GIS

model that enables real-time evaluation of urban streets, incorporating traffic data, pedestrian flow, and environmental attributes to create a holistic street quality assessment

framework34. While these advancements have enhanced the capabilities of street quality research, there remain challenges related to the vast amounts of data involved and the complexity of

processing such data. Large-scale refinement of urban street evaluations, using diverse data types such as POI, heat maps, GIS layers, and 3D models, presents significant difficulties in

data integration and analysis. The variability of data formats, inconsistencies in data quality, and the need for advanced computational tools to process and synthesize these complex

datasets continue to pose obstacles. Nonetheless, the combination of big data and advanced spatial analysis techniques holds great potential for revolutionizing the study of urban street

environments. Recent studies have also emphasized the importance of machine learning (ML) techniques to overcome these challenges. For example, Wang et al. (2023) employed deep learning

algorithms to automatically classify and analyze street images and POI data, significantly improving the efficiency and accuracy of street quality assessments35. Such approaches represent

the next frontier in urban research, where automation and advanced analytics play a key role in handling the complexity of big data in urban planning. STREET QUALITY RESEARCH BASED ON BIG

DATA AND ARTIFICIAL INTELLIGENCE In recent years, the rapid development of big data technologies and artificial intelligence (AI) has significantly transformed the field of urban street

quality research. Traditionally, street quality assessment relied on manual evaluations and qualitative metrics. However, with the advent of big data and machine learning (ML), the scope of

analysis has expanded dramatically, allowing for more efficient, scalable, and objective evaluations of urban streets. Among the most promising techniques are deep learning algorithms,

particularly convolutional neural networks (CNNs), which have demonstrated substantial success in image analysis and spatial pattern recognition. Machine learning, a concept introduced by

Samuel in 1959, has paved the way for intelligent systems that learn from data and improve over time36. Deep learning, a subfield of ML, has taken this further by enabling computers to

process and interpret vast amounts of visual and behavioral data. Algorithms such as Fully Convolutional Networks (FCNs), ResNet and SegNet now allow for sophisticated analysis of images and

spatial data. These models can extract visual features from street-level imagery, such as roads, buildings, sidewalks, trees, and green spaces, enabling researchers to assess street quality

in a manner that was previously impossible37,38. The application of deep learning to urban research is particularly evident in the analysis of streetscape imagery. Through deep learning

techniques, researchers are now able to automatically identify and classify various urban elements, which can then be used to quantify street quality and inform urban design decisions. A

notable project, the “Place Pulse” initiative, launched by MIT’s Media Lab, collected public evaluations of cityscapes through online surveys and paired photos. This project provided a large

dataset (over 1.17 million image pairs) that has become a valuable resource for understanding public perceptions of urban environments39. By analyzing this data, researchers can examine how

different visual elements of streetscapes (e.g., the presence of greenery, building heights, and overall design) contribute to perceptions of aesthetic appeal and urban quality. Zhang

(2020) utilized deep learning methods and the Place Pulse dataset to evaluate urban street quality in Beijing and Shanghai. By correlating visual elements with public perception scores, the

study identified key features that influence perceptions of street environments. The results highlighted the significant role of visual factors such as trees, greenery, and the built

environment in shaping perceptions of street quality and urban livability. Additionally, the study considered multiple dimensions, including beautiful, wealthy, safety, lively, depressing,

and boring, which all play a crucial role in shaping the overall perception of street environments40. Other studies, such as those by Liu et al. (2021) and Li and Guo (2021), have followed

similar approaches, expanding the use of deep learning to other urban quality indicators like safety, walkability, and environmental comfort41,42. In addition to visual perception analysis,

AI and big data are increasingly being used to predict various factors that influence street quality. In 2021, Wang et al. applied deep learning models to predict street walkability in

Beijing, incorporating factors such as street view images, traffic patterns, and environmental data43. Their work demonstrated the ability of AI to assess how street design and environmental

factors impact pedestrian comfort and overall street vitality. This predictive capability allows urban planners to identify areas in need of improvement and to make data-driven decisions

regarding urban infrastructure. The integration of big data and artificial intelligence in street quality research is highly relevant to current trends in urban studies, urban planning, and

smart city development. As cities continue to grow and face increasingly complex challenges related to sustainability, mobility, and livability, AI and big data offer critical tools for

understanding and improving urban spaces. The ability to quantify street quality, predict environmental changes, and optimize urban design based on data-driven insights is central to the

development of more resilient and adaptable cities. Moreover, the insights derived from AI-driven analysis can inform policy-making and urban planning strategies, promoting the creation of

more sustainable, inclusive, and livable urban environments. As the field progresses, there is an increasing emphasis on using AI and big data to create “smart cities” where real-time data

is collected and analyzed to optimize urban living. This research trend aligns with the global push towards data-driven decision-making in urban development, marking a paradigm shift in how

cities are planned, designed, and experienced. MATERIALS AND METHODS STUDY AREA This research focuses on Jinan (Fig. 1), the capital of Shandong Province, covering 7,200 square kilometers

with a population of approximately 9 million. As a key political, economic, cultural, and transportation center, Jinan has seen significant urban development in recent years, the municipal

government has implemented various policies and programs aimed at improving street infrastructure, environmental functions, and the urban landscape. Notably, these initiatives include road

network upgrades, the expansion of green spaces, the introduction of smart city technologies for traffic management, and efforts to enhance pedestrian-friendly environments. These policies

have significantly contributed to the improvement of street quality and the overall urban landscape. Jinan was selected for this study due to its recent urban improvements and availability

of high-quality street image data, which enhances the accuracy of street quality assessments. While other Chinese cities also have ample street image data, Jinan stands out due to its

comprehensive urban development initiatives, making it a representative case for studying street quality in rapidly urbanizing cities. RESEARCH FRAMEWORK The study proposes a large-scale

quantitative assessment method for street spatial visual perception (Fig. 2) to explore the intrinsic connection between different street components and human perception and to further

supplement the results of spatial perception of streets with spatial syntax. The research framework consists of three main processes: (1) Using the free and open API interface of Baidu Maps

to obtain street view images, and constructing the street view image dataset in the study area. (2) The elemental composition of urban streets is extracted using the SegNet semantic

segmentation model, and the human-computer confrontation model is used to quantitatively analyze the spatial visual perception of streets, and the spatial syntax is used to process the urban

street network, and the accessibility of each street is measured on the basis of the 500 m accessibility radius (the average walking distance of residents). (3) The results of street

accessibility analysis and visual perception scores are superimposed and analyzed to establish an evaluation matrix based on the dimensions of “quality evaluation” and “accessibility” to

find out “streets with potential for renewal” in the study area. The results are used to identify “streets with renewal potential” in the study area, and to provide refined technical support

for urban micro-renewal. CONSTRUCTING STREET VIEW IMAGE DATASET In recent years, street view data has been widely used in urban planning studies44, and street view images present

information about urban infrastructure from a pedestrian perspective45. Street view platforms provide APIs that allow users to batch download street view data and provide street view

browsing services. In order to comprehensively measure the visual perception evaluation of street space by residents in the study area, the study set up street view data collection points at

50 m intervals. Throughout the study area, 57,527 Street View collection points were generated within the OSM street network. A total of 30, 511 street view images were collected using

Python and the Baidu Street View Map API (https://api.map.Baidu.com/panorama/v2?ak=YOURKEY). In order to accurately simulate the residents’ perception, the specific parameters for retrieving

Baidu street view images from the API were set as follows: the vertical angle (pitch) was set to 20° and the field of view width (fov) was set to 90°. For each sampling point, a 360°

panoramic view of the street was obtained by stitching the views in four directions. An example of the downloaded street view image is shown in (Fig. 3). DEEP LEARNING BASED SEMANTIC

SEGMENTATION OF STREET VIEW IMAGES This study uses an improved semantic segmentation model based on the encoder-decoder structure SegNet, an open-source deep convolutional neural network

released in 2015 by researchers at the University of Cambridge. SegNet model is due to its demonstrated efficiency and accuracy in processing high-resolution images, and its structural

flexibility allows it to effectively adapt to the complex backgrounds and diverse elements found in urban visual analysis. Compared to other potential models such as U-Net and Fully

Convolutional Networks (FCN), SegNet has significant advantages in maintaining spatial information and detail recovery, which are crucial for accurately identifying various visual elements

in street scenes46. The encoder part of SegNet consists of multiple convolutional and pooling layers, which progressively reduce the size of the feature map and extract abstract features.

The decoder part, on the other hand, consists of an upsampling layer and a convolutional layer, which are used to map the features extracted by the encoder back to the size of the original

image and generate pixel-level classification results47. The semantic segmentation model used in this study was trained and tested on the ADE 20 K dataset, achieving 82.538% pixel accuracy

on the training dataset and 68.432% pixel accuracy on the test dataset. The backbone encoder network in the model is ResNet50, which consists of a convolution (conv) module and an identity

module. The conv module has different input and output dimensions and cannot be placed in series, whereas the identity module has the same input and output dimensions as the other modules

and can be used in series. The structure of the network model is shown in Table 1. The neural network is divided into four parts: input, encoding, decoding, and output. The input layer

reshapes any pixel image to a size of 416 × 416, with the input image elements having three dimensions: R, G, and B. Conv2D is used to construct a convolutional layer to extract features

from the input high-dimensional array. The batch normalization method proposed by Ioffe and Szegedy in 2015 is used to improve the training speed and generalization ability of the network48.

MaxPooling2D is used to reduce the image dimensions and neuron parameters without sacrificing image features. UpSampling2D is used to restore the original image size. The encoder resizes

the image to a size of 43,264 (208 × 208). Softmax calculates the probability of a particular semantic classification for each pixel across 150 categories. The ADE 20 K dataset (Fig. 4) was

used as the training dataset because the images contain 150 objects from everyday life, such as the sky, roads, cars, and plants. Each image is labeled with pixel-level scene segmentation

labels to indicate different objects and regions in the image. Using this dataset to train the model significantly improves the experimental accuracy. PERCEPTUAL SCORING OF STREET VIEW

IMAGES USING A HUMAN- MACHINE ADVERSARIAL SCORING FRAMEWORK This study refers to the human-machine adversarial scoring framework proposed by Yao to predict visual perception scores49, which

is able to more comprehensively assess the quality of different dimensions of visual perception in cities by combining the results of subjective human scoring and automatic machine scoring.

Two-thirds of the sample data in the scoring model were randomly selected for model fitting, while the remaining one-third was used as test data to assess the accuracy of the overall model.

The Place Pulse project at the MIT Media Lab rated street images in six emotional perceptions: beautiful, wealthy, safe, lively, depressing, and boring50,51, which are six dimensions that

provide a comprehensive picture of the quality of visual perception of streets from the residents’ perspective. Additionally, based on these six urban street perception dimensions, an

increasing number of scholars have conducted extensive studies related to human perception of cities52,53. A high or low score for each dimension represents the degree of perceptual

recognition by residents, with higher scores indicating stronger perceptions of the dimension. We further categorized these six dimensions into positive and negative spatial perceptions to

assess the quality of urban streets from the residents’ perspective. Therefore, the six perception types mentioned above were utilized to explore the quality of visual perception of urban

streets with a people-oriented approach. In order to more accurately determine the perception of street quality in the study area, 128 volunteers were recruited to conduct the urban

perception assessment. The schematic diagram of using the human-computer adversarial model to assess the quality of urban streets is shown in (Fig. 5). The volunteers first used the

human-machine adversarial model to rate the six dimensions of beauty, boredom, depression, liveliness, affluence, and safety on a scale ranging from 0 to 6, with higher scores representing

greater congruence with the rated dimensions. This scale was chosen because it provides a balance between perceptual differentiation and ease of understanding for the volunteers. Although

some studies such as Wang et al.(2022) used a scale from 1 to 100, the choice of a 0–6 range was deemed more appropriate for this context which focus on the primary emotional perceptions

rather than highly granular distinctions, it facilitates broader categorization while minimizing cognitive load for participants54. During the implementation of the human-machine adversarial

scoring framework and volunteer assessments, several potential biases could arise. Firstly, volunteer subjectivity and inconsistent scoring standards may lead to variability in the scoring

results, reducing the reliability of the assessments. Volunteers might have different interpretations of the rating scales or personal preferences that influence their evaluations. Secondly,

the Random Forest model, while robust, may encounter challenges such as overfitting, especially if the training data lacks sufficient diversity, which can impair its ability to generalize

to new, unseen data. Additionally, the model might misclassify complex or ambiguous street view images, affecting the overall accuracy of the visual quality scores. To address potential

biases, we implemented the following strategies: Comprehensive Training and Standardization—Volunteers underwent extensive training sessions where clear guidelines and standardized scoring

criteria were provided. This aimed to harmonize the understanding and application of the rating scales, thereby minimizing individual subjective differences. We utilized cross-validation to

evaluate the model’s performance across different subsets of data, ensuring that the Random Forest model maintained high accuracy and stability. Additionally, feature importance analysis was

conducted to identify and prioritize the most influential visual elements, enhancing the model’s interpretability and robustness. Each volunteer was asked to subjectively rate the first

1,000 street view images for each dimension, after which the model constructed a random forest dataset. Random Forest, as an ensemble learning method, effectively handles high-dimensional

data and captures complex nonlinear relationships between variables by constructing multiple decision trees and combining their prediction results. In this study, Random Forest was utilized

to analyze the relationship between volunteer scores and the extracted visual elements in the street view images, thereby predicting visual quality scores for unscored images. Specifically,

the model first used the volunteer-scored data as the training set to learn the influence of different visual elements (such as sky, buildings, plants, etc.) on the scores for each

perception dimension. Then, the trained Random Forest model was applied to predict the scores for the test set, and its performance and stability were evaluated through cross-validation and

feature importance analysis. This process not only enhanced the accuracy of score predictions but also improved the model’s generalizability across different urban environments, enabling

reliable visual quality assessments in a broader range of application scenarios. The selection of 1,000 images was based on considerations of time constraints, volunteer fatigue, and

ensuring sufficient variability in the visual elements. Starting with 1,001 street view images, the model recommended scores for the specified dimensions based on the relationship between

the volunteer’s previous scores and the visual elements in the street view images. STREET ACCESSIBILITY ANALYSIS AND COMBINED STREET QUALITY PORTFOLIO ANALYSIS Spatial syntax allows complex

street designs to be represented as a mathematical graph containing various nodes in order to study the characteristics of their spatial structure55, and is also commonly used in the

analysis of urban spaces with axial and segmental models56. Although axial models have been used in previous studies, they have limitations in performing studies with large-scale urban

applications57. In particular, these models do not consider the scale of urban space58, modeling data cannot be obtained directly from existing urban data, and the modeling time cost is

large, making it difficult to cope with large spatial scales of research. In addition, this study aims to study the accessibility of urban residents during their daily trips, so it is

necessary to consider the daily walking distance of urban residents as the accessibility radius, which is not supported by the axial model. Therefore, this study uses a segmentation model

based on the axial model to measure the accessibility of urban streets. The segmentation model provides three modes to comprehensively analyze the topological geometry, angles and distances

of the street network. The difference between these analysis modes is the different mathematical definitions of the shortest path. In the angular mode of the line segment model, the shortest

path is the path with the smallest combined turning angle between two line segments, which is one of the most commonly used analytical modes in the line segment model. In his study, Hillier

concluded that there is a high correlation between the deflection angle of a roadway network and pedestrian trips, and the angular mode is more in line with people’s perceptions under

natural trips than the other models59. Residents do not have a God-like perspective of urban space and therefore do not deliberately choose routes that are closer together. Instead, streets

with small turning angles are perceived as long and straight, tend to have a stronger sense of direction, may be visually closer, wider in scale, and are habitually more likely to be

selected60. In contrast, Ye explored the accessibility of urban streets in Singapore using the angular pattern of segmental modeling in order to determine the distribution of greenscapes in

space from a human-centered perspective61. Based on the advantages of this model, this study utilizes it to assess the accessibility of urban streets. OSM line data was used as the raw data

for spatial syntax, and GIS tools were employed to apply buffering to the roads. The centerline was then extracted from the buffer to construct a new road network. Finally, street merging,

street simplification, and topology processing were performed on the road network to ensure accurate accessibility calculation. According to the accessibility formula (Eq. 1) proposed by

Hillier & Hanson we calculated the accessibility value for each space62. Equation (1) is as follows:

$$\:{C}_{i}={\sum\:}_{p}^{n-1}{\sum\:}_{q}^{n}\frac{{d}_{pd}\left(i\right)}{{d}_{pd}}$$ (1) \(\:{C}_{i}\:\)is the reachability value of space _i_, \(\:{d}_{pd}\) refers to the shortest path

from space _p_ to space _q_. \(\:{d}_{pd}\left(i\right)\) denotes the shortest path between spaces _p_ and _q_ containing space _i_(p < q, _p_ = 1, 2, 3…, _n_ − 1, q = 2,3, 4…, n). The

study uses depthmapX to visualize street accessibility and applies it to the calculation of various urban street scales. Using the daily walking distance of residents as the reachability

radius, a review of related studies found that the average daily walking distance of residents in first- and second-tier cities in China is about 500 m63. Therefore, we set 500 m as the

reachability radius and calculated the reachability of each street accordingly. The street quality score was obtained by adding the scores of positive street perception and subtracting the

negative perception score, and the street space was comprehensively evaluated by combining with the street accessibility analysis, and finally the street renewal measures were proposed by

analyzing the quality characteristics of the streets under different levels of evaluation. ANALYSIS OF RESULTS ANALYSIS OF STREET VIEW IMAGE COMPONENTS The study utilizes a deep learning

model for semantic segmentation of street view images to identify and categorize various street visual elements at each street data point. For each element type, the proportion within a

street image is calculated as the mean value across all sampled images. The top eight elements by proportion are defined as follows: * _Sky_: The area of the image depicting the sky,

including both clear and cloudy conditions. This encompasses all visible portions above the horizon line, excluding any obstructions such as buildings or trees. * _Building_: All structures

adjacent to the street, including residential, commercial, and industrial buildings. This category includes facades, rooftops, and any architectural elements that are part of the built

environment. * _Plant_: Vegetation elements such as trees, shrubs, and landscaped greenery along the streetscape. This includes street trees, planter boxes, green walls, and any other

plant-based features that contribute to the urban greenery. * _Motorized road_: Roadways designated for motor vehicle traffic, excluding pedestrian areas. This includes lanes for cars,

buses, trucks, and other motorized vehicles, as well as associated infrastructure like traffic signals and signage. * _Vehicle_: Motor vehicles present on the streets, including cars, buses,

trucks, bicycles, and motorcycles. This category captures both stationary and moving vehicles within the street view images. * _Wall_: Walls used to control traffic flow or protect

pedestrian safety, such as isolation walls at the edges of sidewalks, protective barriers in the center of roads. * _Sidewalk_: Pedestrian pathways adjacent to the streets, designated for

foot traffic. This includes sidewalks, crosswalks, pedestrian islands, and any other infrastructure intended for pedestrian use. * _Person_: Individuals walking along the streets, captured

in the images. This includes single persons, groups, and any visible human activity within the street view. The spatial distribution of these elements is illustrated in Fig. 6, demonstrating

their prevalence and arrangement across different urban street areas. By clearly defining each visual element, this study ensures that the categorization process is transparent and

reproducible, facilitating consistent analysis and comparison across different datasets and study areas. ANALYSIS OF VISUAL PERCEPTION OF STREETS BASED ON BSVI The human-computer adversarial

model’s predicted scores for the six emotional dimensions of perception were visualized in Arcgis using kriging linear interpolation (Fig. 7). The urban streets in Jinan received high

scores on beauty and liveliness owing to the well-designed greening efforts and orderly street layout. The wealthy perception score reflects the fact that the economic development of Jinan

urban area is centered on Lixia and Shizhong districts, radiating to the surrounding high-tech district, Tianqiao district and other urban areas, forming an interactive relationship between

the geometric central area and the peripheral areas. Second. The spatial distribution of street safety scores is somewhat similar to affluence scores, i.e., the level of economic development

has a direct impact on law and order and residents’ sense of security, and the more economically developed, the better the law and order is in relative terms. Most of the places with high

depressing perception scores are located in old urban areas, where relatively narrow streets as well as dense building complexes cause residents’ depressing perceptions to increase. Areas

with high boring perception scores are mainly distributed along the city’s main roads, indicating that they are built in a monolithic way, which may be related to the urban function of the

area. Instead of creating landscape features, customization and uniformity are pursued during the planning and construction of major urban roads. MULTIPLE REGRESSION ANALYSIS OF VISUAL

PERCEPTION AND VISUAL ELEMENTS This study explored the impact of various components of urban streets (sky, Motorized Roads, vehicles, plants, buildings, walls, sidewalks, and pedestrians) on

different visual perception dimensions (beautiful, wealthy, safety, lively, depressing, and boring) through multiple linear regression analysis. The regression analysis results are shown in

Table 2, where the asterisk next to the coefficient indicates its statistical significance level (***_P_ < 0.001: indicates significant results at a 99.9% confidence level, with

extremely high statistical significance, **_P_ < 0.01: indicates that the result is significant at a 99% confidence level and highly significant, *_P_ < 0.05: Indicates that the result

is significant at a 95% confidence level). This study utilizes regression analysis to uncover the impact mechanisms of various components of urban streets on different dimensions of visual

perception, providing data-driven insights for urban planning and street design. By optimizing greenery, adjusting wall designs, improving roads and sidewalks, and promoting pedestrian

activities, the visual quality of streets and residents’ life satisfaction can be effectively enhanced, thereby fostering more human-centered and sustainable urban development. * _Optimize

greenery design_: Research results indicate that plant coverage has a significant positive impact on perceptions of beauty, liveliness, and safety. Therefore, urban planners should

prioritize increasing street greenery, such as planting trees and installing green walls, to enhance the overall visual quality of streets and residents’ satisfaction. * _Adjust wall

design_: Walls have a significant negative impact on multiple dimensions of visual perception, including beauty, liveliness, safety, wealth, and boredom, indicating that wall designs may

need optimization. It is recommended to use more aesthetically pleasing and functional wall materials or reduce the use of walls to minimize their negative impact on visual quality. *

_Improve motorized roads and sidewalks_: Good motorized road and sidewalk design positively influences multiple perception dimensions, including beauty, liveliness, safety, and wealth. Urban

planning should focus on maintaining motorized roads and expanding sidewalk designs to enhance street accessibility and visual quality. * _Promote pedestrian activities_: Pedestrian

activity has a significant positive impact on perceptions of liveliness and safety. By increasing recreational facilities on streets and establishing convenient pedestrian pathways,

pedestrian activities can be promoted, thereby enhancing the overall liveliness and safety of streets. OVERLAY ANALYSIS OF STREET ACCESSIBILITY AND STREET QUALITY The accessibility of urban

streets in Jinan city is presented based on the daily walking distance of residents of 500 m (Fig. 8), the range of 0 to 6 in the map indicates the level of road accessibility, with 0

representing the lowest accessibility and 6 representing the highest accessibility. Most of the highly accessible streets are urban arterials, which bear the main traffic flow of the city,

connecting urban areas with important regions, with high vehicle and pedestrian traffic, and are usually designed with multiple lanes. Secondly, compared with the central and western

regions, the distribution of highly accessible streets in the southeast region is relatively sparse. With the advancement of urban development planning, urbanization in the Southeast region

has accelerated, and street accessibility has been improved by increasing the number of lanes, constructing pedestrian paths, and strengthening transportation infrastructure. Overall, major

transportation arteries basically cover the urban area, with a well-developed transportation network and high connectivity within and outside the region, which promotes economic development

and facilitates the flow of people and goods. Combining the measurement of street quality and the overlay analysis of street network accessibility, the aim is to evaluate the current spatial

status of streets using the “accessibility-quality” evaluation dimension, and to discuss which streets have high quality and accessibility and good spatial quality of places, and which

streets have high pedestrian accessibility but unsatisfactory quality, indicating that although they have good potential for pedestrian activities, they still need to be improved in terms of

perceived quality. The evaluation of streets with high accessibility and good spatial quality of place, and those with high walkability but poor quality, indicates that although they have

good potential for walking activities, they still need to be improved in terms of perceived quality, thus identifying potential problems in improving the quality of the streets. In this

overlay analysis, the median value of road accessibility in the study area was used to categorize accessibility into “high accessibility” and “low accessibility”, and the median value of

street quality measures was used to categorize spatial quality into “high quality” and “low quality”. The median value of the street quality measurement results is used to categorize the

spatial quality into “high quality” and “low quality”, and the two dimensions can be integrated to divide the streets into four major categories: high quality/high accessibility; high

quality/low accessibility; low quality/high accessibility; low quality/low accessibility; and their distribution is shown in Fig. 9. High quality/accessibility: These streets score well in

both environmental quality and pedestrian accessibility, offering strong spatial potential. They are relatively few and primarily located in urban centers. High quality/low accessibility:

found mainly at district junctions, these streets feature high greening rates and well-developed infrastructure, contributing to strong quality scores. However, their sparse surrounding road

networks result in low pedestrian accessibility. Low quality/high accessibility: these streets, often situated southwest of the city center, benefit from good pedestrian accessibility but

lack essential street furniture and greenery, leading to lower quality scores. Low quality/low accessibility: these streets require particular attention in urban planning. Predominantly

located on the city’s periphery, they are characterized by aging buildings, low structures, and fragmented street networks, which hinder accessibility. To enhance these areas, urban planning

efforts should prioritize functional improvements, building façade renovations, and increased greenery. Strengthening street quality and vitality while gradually expanding the pedestrian

network will be key to their transformation. CONCLUSION This study constructs a method for evaluating the visual perception of urban streets using spatial syntax theory and artificial

intelligence technology, with a human-centered approach. The evaluation of street quality and micro-renewal potential is carried out through accessibility analysis and overlay analysis of

urban streets. Additionally, the study employs a visual perception score combined with multiple regression analysis to reveal the influence mechanisms of street components on visual

perception. The methodology applied in this study provides an effective way to measure the spatial quality of urban streets, offering data-driven suggestions for improving street quality.

The analysis of spatial perception and visual elements can be applied to various stages of urban street development and management, contributing valuable insights for future urban planning.

As an emerging technology in urban analysis, this approach has significant advantages in large-scale, human-centered planning and design. First, in terms of data acquisition, using

streetscape images to study the spatial quality and vitality of urban spaces is more time- and cost-efficient than traditional methods such as manual surveying or photography. Second, in

terms of research methodology, using large-scale streetscape data for quantitative analysis offers more reliable results than traditional qualitative research or small-scale audits. Third,

from a research perspective, combining objective streetscape data with residents’ subjective perceptions allows for efficient and scientifically informed urban analysis, addressing urban

issues in a problem-oriented manner. This approach contributes to a more rational and data-driven process in urban planning and design. Furthermore, the findings of this study have practical

applications for policymakers and urban planners. For instance, the identification of streets with high accessibility but low visual quality can inform targeted interventions to enhance

these areas. By implementing targeted greening measures, such as increasing street tree density or adding green walls, policymakers can improve pedestrian safety and overall street

aesthetics. Additionally, the insights gained from the multiple regression analysis can help in designing micro-renewal projects that address specific visual and functional deficiencies,

thereby fostering more vibrant and sustainable urban environments. Despite the advantages of this approach, the study does have some limitations. Due to constraints in data sources, the

street view data was collected and photographed from the viewpoint of the roadway, which may introduce discrepancies when compared with the actual pedestrian perspective, as much of the

public space used by people for daily activities is primarily located along walking paths. Additionally, limitations in the machine learning algorithms and the resolution of the street view

data meant that certain key elements, which could impact spatial quality, were not included in this analysis. This resulted in some potential inaccuracies and limitations in the quality

evaluation, we will incorporate advanced analysis algorithms to address these shortcomings in the future research. Furthermore, the data from different times could be used to analysis the

trends in the spatial quality of urban streets over time. By analyzing urban street planning and development from both spatial and temporal dimensions, we aim to predict future trends and

improve the accuracy of urban planning decisions. Future studies could also explore the integration of real-time data sources and temporal street view images to capture dynamic changes in

urban environments, thereby providing a more comprehensive understanding of how visual perception evolves over time. DATA AVAILABILITY The datasets used and/or analyzed during the current

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work was supported by the National Natural Science Foundation of China, grant number (No. 41801308). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * School of Surveying and Geo-Informatics,

Shandong Jianzhu University, Jinan, 250101, China Mingyang Yu, Xin Chen, Xiangyu Zheng, Weikang Cui, Qingrui Ji & Huaqiao Xing * Inspur Smart City Technology Co., Ltd, Jinan, 250014,

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also search for this author inPubMed Google Scholar CONTRIBUTIONS Mingyang Yu and Xin Chen wrote the main manuscript text together. All authors reviewed the manuscript. CORRESPONDING AUTHOR

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