Street space is the main space that affects tourists' experience of tourism sites. The visual quality of street space is crucial to the development of tourism. However, the evaluation method of visual quality needs further exploration. This paper selected Gulangyu, the famous tourism site in Xiamen, as a study case. First, we established a quantitative model of visual quality combining the existing research on street space and the visual elements of tourism sites. Then, we collected street view data of each intersection by traveling like tourists, corrected imaging parameters, and encoded the street view images. Second, based on the deep learning method (Fully Convolutional Networks, FCN), we segmented the collected street view images semantically and extracted the visual elements in street view data. Finally, by combining with GIS, we set up a geographic information database to analyze the visual and spatial characteristics of each sampling point's visual elements. This database was aimed at providing a basis for further evaluation of the visual quality of street space. It was aggregated using the street line as the smallest unit. In our study, we calculated the visual quality indicators to evaluate the street space in Gulangyu. The results show that: (1) There is obvious spatial differentiation in the visual elements of street space in Gulangyu; (2) Building density, street width, and vegetation sketches are the basic visual elements that shape the visual quality of street space; (3) The distribution of botanical parks, major docks, and commercial facilities significantly impacts the street space's visual quality. In detail, green plants, buildings, roads, sky, and street facilities show the differences between a center and a roundabout. While the distribution of pedestrians shows differences between the east and the west. The green view rate, enclosure, sky openness, and diversity of street space also have obvious center-roundabout spatial differentiation. Moreover, there is an obvious spatial agglomeration effect in the green view rate, crowding degree, and diversity of street space. The agglomeration points are mainly parks, docks, and commercial streets. The method in this paper provides a new collection method in street visual quality evaluation. The visual element extraction accuracy based on FCN is fairly high, which can provide a reference for street view images and other types of image data analysis. This paper provides a valuable reference for street space management and planning, resource integration and allocation, human flow guidance, and regulation in tourism sites.

Accurate identification of visual factors affecting environmental safety perception provides important support for improving urban traffic environment and enhancing pedestrian travel safety. However, it is difficult to quantify environmental safety perception in complex scenes on a large scale in existing studies. Therefore, this study uses image semantic segmentation and object detection techniques to extract visual factors from streetscape images and constructs a road safety perception dataset by manual scoring in combination with deep learning methods. The influencing factors of environmental safety perception are also identified based on light gradient boosting machine algorithm and SHAP interpretation framework. In our study, the Anning District college cluster in Lanzhou City, a canyon city with a special road environment, is selected for the empirical study. Results show that: (1) The safety perception scores of colleges and commercial streets are high, while those of urban roads are generally low; (2) The image ratios of sky, sidewalk, road, and tree are the four factors that have the greatest influence on environmental safety perception, among which the image ratio of sky is linear, the image ratios of sidewalk and tree are similar to a logarithmic function, and the image ratio of road is similar to a quadratic function; (3) The proportion and number of visual factors have an interactive effect. A reasonable distribution of visual factors helps to create good spatial sightlines and suitable behavioral spaces, thus enhancing the perception of environmental safety.

Understanding the impact of urban environment on human perception has important humanistic reference value for rational urban planning and layout. The urban environment is a dynamic and complex system with spatial heterogeneity. Due to the limitations of research methods in the complex urban environment, it is difficult to analyze the impact of key elements of the environment on human perception comprehensively and precisely in previous urban perception studies based on street view images. In this study, the center of Wuhan is chosen as the research area. First, the street view images are segmented into urban ground objects by using Fully Convolutional Networks, coupled with perception scoring data and Random Forests algorithm to establish six types of urban perception models. Then, based on the Shapley value method, the impact of various urban ground objects on human perception in the forest model is decomposed, and the key elements of the urban environment are identified. Finally, combined with the decomposition results, the applicability and advantages of the Shapley value method in the nonlinear model are explored. The results show that the Shapley value method can effectively consider the spatial heterogeneity of urban physical environment and accurately and quantitatively express the impact of various ground objects on human perception in different scenarios. Urban buildings, sky, and green space are the three categories that have the greatest degree of impact on human perception. And the volume and distribution form of ground objects affects human intuitive visual experience of ground objects, which in turn affects the specific impact role of ground objects on human perception. The larger the image proportion of the ground objects or the more continuous the distribution of the ground objects, the greater the impact of ground objects on human perception. At the same time, because of the influence of the spatial heterogeneity of the urban physical environment, the impact degree and form of the main ground object types on various perceptions are significantly different. The relationship between buildings and human perception is nonlinear and has an obvious monotonically increasing or decreasing form. Green space has a nonlinear relationship with positive perception and a linear decreasing relationship with negative perception. Based on the interpretability approach, this study analyzes the impact of key elements of urban environment on human perception and explores the interpretability issues in urban perception models, which can provide methodological reference and theoretical basis for urban perception related research, as well as reference for urban planning and landscape design.

Streets are an important attraction for urban tourism. Exploring the influence of street landscape color characteristics on tourists' emotional perception holds important reference value for the rational planning and layout of urban street landscape. This study takes the built-up area within the third ring road of Xi'an city as a study case, and employs the Full Convolutional Neural Network (FCN) and Random Forest (RF) algorithms to construct an emotional perception dataset of street images. We use the streetscape images as the basis to extract the color features of the streetscape using machine learning algorithms, and color quantifiers are constructed and spatially visualized; The RF regression algorithm is used to explore the relationship between streetscape color characteristics and tourists' emotional perception, and the optimal color characteristic parameters are derived. The results show that: (1) There is a distinct spatial distribution pattern of tourists' emotional perception. The emotions of beauty and liveness gradually increase from the central area outward, and emotions of safety and wealth emotions score higher in the area within the second ring road outside the main city. While boring emotions score lower in this area, and depressing emotions gradually decrease from the central area outward. This suggests that the spatial distribution pattern of emotional perception shares somewhat homogeneity between tourists' emotional perception in non-routine environment and residents' perception in familiar environment; (2) The color characteristics of the streetscape show a complex non-linear relationship with tourists' emotional perception. For example, color complexity has less effect on emotions of beauty and liveness compared to color coordination and has a greater effect on emotions of boredom, depression, safety, and wealth than color coordination. Moreover, when the value of color complexity is 0.86 and the value of color coordination is 0.84, tourists can obtain better emotional perception across six dimensions; (3) Under non-routine conditions, the more significant the color characteristics of the street landscape, the better the emotional perception of visitors. Theoretically, this study confirms the conclusion that the more colorful environment leads to better experience for tourists; and methodologically, this paper not only expands the traditional text-based and manually-assigned research methods in the field of tourism emotion, but also enriches the application of streetscape big data and machine learning methods in the field of tourism. This study provides a reference for city managers to understand tourists' visual preferences for streetscapes and to optimize streetscape design.

Understanding the urban spatial pattern from the perspective of the “Production-Living -Ecological” function not only paves the way to the optimization of land spatial structure, but also reflects the internal functional form and combination mode of urban land. However, in the past, the recognition of urban “Production-Living -Ecological Spaces” (PLES) mainly relied on remote sensing images, Point of Interest (POI), and land use data, and there was a lack of three-dimensional information within a city. Street View Images (SVI) can reflect the characteristics of the streets in the city and capture large-scale and high-resolution objective measurements of the physical environment within a street from a close-up view. Therefore, based on the semantic features of the scene extracted from the SVI, this paper proposes a method of identifying PLES in the central urban area and analyzing the importance of different features of the PLES. Taking the Fourth Ring Road of Chengdu as the study case, the classification system of PLES is constructed based on POI data, and the proportion of PLES is calculated at each SVI sampling point. The eXtreme Gradient Boosting (XGBoost) algorithm is used to identify the urban PLES, and a comparative test of model accuracy is also carried out. The spatial distribution of PLES in the study area is analyzed from three scales, i.e., road network, 500-m grid, and traffic analysis zone. The SHapley Additive exPlanation (SHAP) method is introduced to identify the important features that contribute to PLES. The results are as follows: (1) The proposed method of identifying PLES based on SVI in this paper has a high accuracy. The R² of the model for identifying PLES reaches 0.6, indicating the feasibility of SVI for identifying PLES; (2) The spatial pattern of PLES reveals that the study area is dominated by production-living spaces, which are large in number and distributed in pieces in the study area. The number of units dominated by ecological space is small, and they are mainly distributed in large parks; (3) Among the semantic features of the seven types of scenes, street openness and motorization level have the greatest impact on the formation of the PLES. Based on PLES, this study uses SVI data to conduct a fine-scale analysis of land use in central urban areas and determine the types of urban land. This paper enriches the data and methods of PLES identification and provides a new tool for the optimization of urban spatial structure and development decision making.

With the acceleration of population aging, the urban built environment for the elderly faces severe challenges. Urban street environments, one of the most frequently used places by the elderly, require high-quality construction, which is vital for realizing an age-friendly society. However, few studies have focused on the spatial effects and influencing factors of urban street environment quality for the elderly from a large-scale and human perspective, resulting in difficult practical applications. Therefore, this study took Tianhe district, Guangzhou as a study area, using machine learning and deep learning technology to evaluate the urban street environment quality for the elderly and analyze its spatial distribution and influencing mechanisms. Based on 14 916 human-centric street view images taken by panoramic cameras, semantic segmentation and object detection techniques were used to extract environmental elements. Greenness, openness, crowdedness, enclosure, sidewalk ratio, and scene diversity were obtained finally as explanatory variables in this study. A human-machine adversarial scoring system was constructed for the age-friendly street environment quality assessment. Twenty-two elderly volunteers were invited to rate their sense of walkability, vitality, security, belonging, and pleasure from 1 000 randomly selected images. A residual neural network 50 (ResNet50) was used to predict the urban street environment quality in the Tianhe district based on street view images and crowd-sourced data. The spatial autocorrelation was measured by global and Local Moran's I. Ordinary Least Square regression model (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM) were established to analyze the influence mechanisms. Results show that: (1) Using human-centric street view images, machine learning, and spatial statistics methods, this study conducted a fast, large-scale, and precise age-friendly street environment quality assessment and accounted for spatial heterogeneity to identify its key influencing factors; (2) There was a moderate degree of spatial aggregation of different street environment qualities for the elderly in the Tianhe district. For older people, commercial streets and streets near low-density residential areas were associated with higher levels of walkability, activity, sense of safety, and pleasure. Although waterfront streets had higher levels of activity and security, the level of pleasure was low. Streets near high-density residential areas were found to have lower levels of activity level, sense of safety, and pleasure. The sense of belonging was higher in commercial streets and lower in streets close to residential areas; (3) The effects of environmental factors on different street environment quality indexes for the elderly were different. Greenness, openness, and enclosure were important factors while visual crowdedness, sidewalks, and scene diversity played a weak role. Greenness had a positive effect on activity level and sense of safety, but a negative effect on pleasure and sense of belonging. Openness was positively correlated with walkability, pleasure, and sense of belonging, and negatively correlated with activity levels. Enclosure had negative effects on all indicators, especially the sense of belonging. These results reveal the spatial association, heterogeneity, and influencing mechanisms of the street environment quality for the elderly based on human-centric street view images, machine learning, and deep learning techniques on a large urban scale. It shows a feasible paradigm to analyze the street environment for the elderly, providing practical implications to build resilient streets more conducive to an age-friendly society. It's of great value for policy-making, urban planning, and management.

Urban green spaces are critical components of urban ecosystems, playing an irreplaceable role in improving the ecological environment and enhancing quality of life. High-precision identification of urban green spaces is fundamental for urban renewal and optimizing green infrastructure. However, research on the identification and spatial heterogeneity of green spaces in megacities remains relatively limited. This study, taking Xi'an as an example, integrates urban street view images and GF-2 (Gaofen-2) satellite imagery, employing methods such as ISODATA classification, K-Means classification, and convolutional neural networks to achieve multi-dimensional, downscaled, and high-precision identification and analysis of green spaces. The results indicate the following: (1) The K-Means classification method demonstrates significantly higher accuracy (84.5%) compared to the ISODATA classification method (62.4%) and more accurately maps the spatial characteristics and heterogeneity patterns of green spaces. The green space coverage identified by the K-Means method is 0.277 0, which is lower than the 0.360 7 identified by ISODATA. (2) The average Green View Index (GVI) of streets in Xi'an's main urban area is 0.156 0, indicating a generally good level of street greening. However, there is notable polarization across different roads, with 30% of sampling points having a GVI below 0.080 0. Overall, the GVI of higher-grade roads is greater than that of lower-grade roads, following the trend: primary roads > secondary roads > trunk roads > tertiary roads. (3) There is a positive correlation between the GVI of streets and the vegetation coverage in their surrounding areas in Xi'an's main urban area. However, this correlation weakens in certain road sections, reflecting differences between vertical cross-sections and overhead views of the streets. Combining these perspectives provides a more accurate assessment and quantification of urban green spaces. This study provides a reference for green space planning, green infrastructure construction, and smart management in Xi'an, as well as technical guidance for high-precision identification and spatial analysis of urban green spaces in other cities.

The street network channels people's routine activities, which in turn affects the distribution of crime incidents. Therefore, the street micro-environment is crucial to the fine-grained understanding and explanation of the spatial distribution of street crime. In the existing research on street micro-environment extraction from street view images, semantic segmentation technique is often used to calculate the pixel proportions of various elements, without identifying individual features and objects on street. Recently, some scholars have combined semantic segmentation and object detection technologies to extract complex street environment features, generating both pixel proportions and object counts. However, no research has compared the associations between street crime and the micro-environment features extracted by the two methods. In order to explore this issue, this study used the above two methods to extract street micro-environmental features from Baidu Street View images. The first method used semantic segmentation technique to extract pixel proportions of all features. The second method combined a semantic segmentation and an object detection technology to extract the pixel proportions or counts of individual features, e.g., sidewalk, building, wall, fence, tree, and grass were measured as pixel proportions, and people and light posts on street were measured as counts. After controlling for land use mixture, concentrated disadvantage, street density, length of street segment, and facilities that attract or generate crime, zero-inflated negative binomial regression models were constructed to assess the impacts of the street micro-environmental features on street property crime, such as street theft and pickpocketing. The above two street micro-environment measurements were added to the models separately, and their influences on street property crime were then compared. The results show that: (1) compared with the conventional semantic segmentation method, adding the street micro-environment features extracted by combining semantic segmentation and object detection techniques increased the model performance by 7%. Specifically, the number of pedestrians obtained by the object detection method can better reflect the actual size of people on the street than the pixel proportion of pedestrians obtained by the semantic segmentation method, resulting a stronger association between pedestrians and street property crime. Its regression coefficient increased from 0.09 to 0.32, and the order of the absolute value of the regression coefficient increased from the tenth place to the third place; (2) the street micro-environment features extracted from street view images can effectively explain the occurrence of street property crimes. Crime targets and activity support features can significantly affect street property crime. Specifically, there were significant positive associations between the number of pedestrians on the street and street property crime, and significant negative associations between pixel proportions of sidewalks, buildings, trees, lawns, and green-rate, and the street property crime. This research adds more evidence to the literature of crime geography and environmental criminology, especially for Crime Prevention Through Environment Design (CPTED).

Street crimes pose threats to human life. Previous research tackling street crimes such as public thefts and provocations were often conducted at the neighborhood or even larger levels, thus ignoring the fine characteristics of the built environment at the address level, as is advocated by the theory of Crime Prevention Through Environmental Design (CPTED). It has been well established that the micro built environment at the address level has a direct or indirect impact on the occurrence of various types of crime. However, the measurement of the micro built environment, often called environmental auditing, has been challenging. Most of the previous studies relied on survey samples to characterize the built environment. They suffered from two limitations: incomplete characterization of the built environment and sparsity in spatial coverage. Baidu street view imagery is a new data source that captures built environment at the micro level. It often captures images in four different directions at a given location on the street, and it covers virtually all the drivable streets. This study used a full convolution deep learning image segmentation algorithm to extract the micro characteristics of the built environment from Baidu street view images and applied them to study crimes at the small address level. Eight variables including tree, road, sidewalk et al. were selected to show the differences of micro built environment in the study area. Bayesian logistic regression models were used to evaluate the impact of micro-built environment factors on public thefts and provocations by controlling other relevant criminological factors. Results show that the model performance was improved after adding the micro characteristics. Additionally, we found that places with more trees hence less visibility were more prone to public theft cases, and places with more roads and sidewalks were less prone to public theft cases, when compared with provocative cases. In sum, the convolution deep learning image segmentation algorithm is effective in extracting street-view-derived variables, and these variables added new dimensions to crime analysis at the micro spatial scale. This study not only contributes to the literature in crime geography, but also bring new insight for crime prevention following the principle of CPTED.

The spatial differentiation of housing price is an external expression of the unbalanced allocation of urban spatial resources. It is of great significance for formulation of residential planning and housing price control policies to understand the leading influencing factors and their spatial differentiation characteristics. Existing studies seldom consider the impact of environmental quality on housing price and the different scale effects of influencing factors. In response to the above problems, the study introduces street view imagery, adds environmental characteristic variables based on the traditional Hedonic price model, and constructs Multiscale Geographically Weighted Regression (MGWR) model to explore the impact of environmental characteristics on housing price. The spatial pattern of coefficients of other control variables are analyzed and the spatial differentiation characteristics of each variable are summarized. The main conclusions are: (1) The environmental characteristics measured by street view imagery are more consistent with people's real perception of the living environment, and the research conclusions can provide more refined strategies for improving the quality of the living environment. The MGWR model has a fitting effect on the spatial differentiation of variables closer to the real value, and it can describe the difference of action scales of different variables, which is helpful to formulate targeted planning strategies for specific regions; (2) The housing prices on Xiamen Island show significant clustering characteristics, and are distributed in a "band" structure along the city’s core development axis; (3) The three environmental characteristics variables have significant positive effects on housing prices, and the effects are close to the global scale. The impact of street view green viewing rate is the strongest, followed by sky openness and relative walkability index; (4) By summarizing the spatial differentiation rules of the coefficients of each variable, it is found that the dominant influencing factors of housing price in different characteristic areas are different. The core areas are mainly affected by traffic and education factors; the old urban areas are mainly affected by environmental quality and building quality factors; the new urban areas are mainly affected by location and living facilities.

The harms of excessive drinking are important issues faced by China and other countries around the world. Previous studies have identified certain relationships between drinking behaviors and environmental factors, with limited consideration of the dynamic geographic context and spatiotemporal heterogeneity of these relationships. This study constructs a theoretical framework for the relationships between excessive drinking and geographical environment factors (i.e., alcohol availability, spatial disorder, and social disorder). Using multi-source spatiotemporal data, including the alcoholism cases received by Guangzhou Emergency Medical Command Center and mobile signaling data, the study explores the spatiotemporal patterns and the influencing factors of excessive drinking in a dynamic geographic context. Results show that: (1) Excessive drinking events mostly occur on weekends, at night, and in urban commercial centers. Its spatial distribution is balanced in the daytime but congregated in the nighttime; (2) Excessive drinking is influenced by alcohol availability (positively correlated with the density of KTVs and traffic stations). The positive impact of KTVs is prominent in the old urban area in the daytime and is prominent in more areas in the nighttime. The positive impact of traffic stations is mainly observed at the edge of central urban area and in suburban areas, and is relatively stronger in the daytime; (3) Excessive drinking is also affected by certain spatial disorder factors (positively correlated with the land development level), but has no significant relationship with building age and streetscape messiness. The positive impact of land development level is prominent in the new urban scenic core area and the urban villages at the urban-rural fringe, with a stronger impact in the daytime; (4) The relationship between excessive drinking and social disorder shows greater spatiotemporal differentiation and complexity. Overall, excessive drinking is negatively correlated with the proportion of nonlocal-born population, and positively correlated with the proportion of low-educated population and residential rent; (5) By analyzing different leading factors influencing excessive drinking in the daytime or nighttime, four types of spatial units can be identified, allowing for respective interventions in these zones to reduce the risk of excessive drinking. This study reveals the relationships between excessive drinking and geographical environment in a dynamic geographic context and at a smaller spatial scale, which fills the gap of lacking smaller-scale analysis, spatiotemporal heterogeneity perspective, and consideration of dynamic geographic context in previous studies. Our results provide insights for alcohol policy making, regional security and medical resource allocation, spatial planning, and environmental intervention related to health issues.

[Objectives] Although the use of street view data to calculate the Green View Index (GVI) has emerged as a method for evaluating urban greening levels, systematic research on the spatiotemporal dynamics of GVI remains limited. [Methods] This study explores the spatiotemporal characteristics and influencing factors of urban GVI using street view big data, providing a new method for assessing urban street greening levels. This study proposes the GSENet semantic segmentation model for calculating and analyzing the GVI in Lanzhou's main urban area. The GSENet model incorporates a GSE-Block feature calibration module within its encoder, combining spatial and channel attention mechanisms. The decoder adopts an efficient self-attention module (Mix-transformer), which introduces a scaling factor and replaces the fully connected layer with a 1×1 convolution, combining the global modeling capability of Transformers with the local processing ability of convolution. Using the GSENet model, this study calculates the GVI of Lanzhou's main urban area based on Baidu Street View data and explores its spatiotemporal variation patterns through hotspot analysis, statistical analysis, and correlation analysis. [Results] The results reveal several key findings: (1) Utilizing ResNet50 as the backbone, the GSENet model achieves a Mean Intersection over Union (MIOU) of 74.7%, outperforming mainstream models such as PSPNet and DeepLabV3. The model demonstrates superior performance in identifying large-area categories such as vegetation and buildings, achieving an F1 score of 0.95. (2) Between 2019 and 2023, the average GVI increased by 2.3% compared to the period from 2014 to 2018. Notably, 70.9% of the sampled points showed a positive GVI trend, although only 8.4% experienced an increase greater than 10%. Anning District recorded the most substantial improvement, with a GVI rise of 3.5%, while Chengguan District saw the smallest growth, at only 1.9%. Spatial analysis identified that the central-western and northeastern parts of the study area experienced significant GVI increases, particularly in regions surrounding universities. In contrast, GVI declined notably in commercial centers and transportation hubs. (3) The influence of street view features and social factors on GVI changes exhibits spatiotemporal heterogeneity. Building density shows a negative correlation with GVI changes. The correlation between road width and GVI changes is relatively weak while the correlation between population density and GVI changes varies across different scales, with a stronger positive correlation at the street scale. [Conclusions] The experimental results highlight the effectiveness of this research in enhancing the perceived greening of urban streets. Furthermore, the findings provide valuable insights for urban planners aiming to optimize green space distribution and improve urban environments.

[Objectives] The quality of the leisure environment is a critical factor influencing residents' leisure experiences and participation, and it is closely related to the vitality of urban areas and economic development. Therefore, exploring how environmental quality influences the vitality of leisure space is crucial for promoting urban development. [Methods] A human-centered approach is adopted to construct a research framework for exploring the relationship between leisure environment quality and leisure space vitality based on image-text fusion perception. Online review texts and street view images are used to comprehensively perceive the leisure environment quality of the city. Natural language processing and semantic segmentation techniques are used to assess the leisure environment quality, while mobile signaling data is utilized to quantitatively measure the vitality of leisure spaces through user trajectory semantic modeling. Finally, using an Optimal Parameter-based Geographical Detector (OPGD), an in-depth analysis is conducted on the impact mechanisms of individual leisure environment quality factors and their interactions with the vitality of leisure spaces at global and local spatial scales in Nanjing. [Results] The findings reveal that: (1) The spatial distribution of leisure space vitality exhibits a "single-core-multi-center" pattern. The vitality in the main urban area is concentrated around the Xinjiekou commercial district, while Jiangbei District forms a "three-point" pattern with interactions between the two ends and the center. In the Xianlin area, high-vitality zones are distributed around the university town, while in the Dongshan area, they are located along the Shuanglong Avenue corridor. (2) On a macro scale, the leisure space vitality of Nanjing is indirectly dominated by economic levels. On a local scale, the influence of 14 leisure environment quality factors on leisure space vitality demonstrates significant regional heterogeneity. However, in municipal and district-level core areas with high leisure space vitality, the effects of these environmental quality factors are all significant. (3) The formation mechanism of leisure space vitality in Nanjing is closely related to regional geographical location, population density and composition, and economic income levels. [Conclusions] The analysis of Nanjing indicates that the exploration of leisure environment quality through image-text fusion perception enhances the systematic and comprehensive understanding of the factors influencing leisure space vitality and its mechanisms. This provides a scientific basis for optimizing the quality of the urban leisure environment and enhancing the vitality of leisure space.

[Significance] Street View Image-based Visual Place Recognition (SV-VPR) is a geographical location recognition technology that relies on visual feature information. Its core task is to predict and accurately locate unknown locations by analyzing the visual features of street view images. This technology must overcome challenges such as appearance changes under different environmental conditions (e.g., lighting differences between day and night, seasonal variations) and viewpoint differences (e.g., perspective deviations between vehicle-mounted cameras and satellite images). Accurate recognition is achieved through calculating image feature similarity, applying geometric constraints, and related methods. As an interdisciplinary field of computer vision and geographic information science, SV-VPR is closely related to visual positioning, image retrieval, SLAM, and more. It has significant application value in areas such as UAV autonomous navigation, high-precision positioning for autonomous driving, construction of geographical boundaries in cyberspace, and integration of augmented reality environments. It is particularly advantageous in GPS-denied environments. [Analysis] This paper systematically reviews the research progress of visual location recognition based on street view images, covering the following aspects: First, the basic concepts and classifications of visual place recognition technologies are introduced. Second, the foundational principles and categorization methods specific to street view image-based visual place recognition are discussed in depth. Third, the key technologies in this field are analyzed in detail. Furthermore, relevant datasets for street view image-based visual place recognition are comprehensively reviewed. In addition, evaluation methods and index systems used in this domain are summarized. Finally, potential future research directions for SV-VPR are explored. [Purpose] This review aims to provide researchers with a systematic overview of the technological development trajectory of SV-VPR, helping them quickly understand the current research landscape. It also offers a comparative analysis of key technologies and evaluation methods to support algorithm selection, and identifies emerging challenges and potential breakthrough areas to inspire innovative research.