Baidu Street View Research Articles

Evaluating the Impact of Elementary School Urban Neighborhood Color on Children’s Mentalization of Emotions through Multi-Source Data

To address the challenge of quantitatively assessing the mentalization of emotions in color design schemes, this study uses Baidu Street View images and deep learning, integrates multi-source data, and innovatively constructs a color data model based on a comprehensive color indicator system for the quantitative assessment and visual representation of how the color environments of elementary school urban neighborhoods impact children’s mentalization of emotions. This model systematically incorporates physical color indicators, integrates elements such as perceptual frequency, and provides a novel perspective for color planning. The study’s results reveal that color metrics significantly impact children’s mentalization of emotions across multiple dimensions, with gender and age emerging as important influencing factors. Additionally, significant correlations were found between color and environmental elements such as building façades, roads, and signs. The study provides urban planners and architects with a practical color data model and recommendations for the revitalization of elementary school urban neighborhoods, offering a scientific basis for optimizing color design.

Portraying on-road CO2 concentrations using street view panoramas and ensemble learning

A significant reduction in carbon dioxide (CO2) emissions caused by transportation is essential for attaining sustainable urban development. Carbon concentrations from road traffic in urban areas exhibit complex spatial patterns due to the impact of street configurations, mobile sources, and human activities. However, a comprehensive understanding of these patterns, which involve complex interactions, is still lacking due to the human perspective of road interface characteristics has not been taken into account. In this study, a mobile travel platform was constructed to collect both on-road navigation Street View Panoramas (OSVPs) and the corresponding CO2 concentrations. >100 thousand sample pairs that matched “street view-CO2 concentration” were obtained, covering 675.8 km of roads in Shenzhen, China. In addition, four ensemble learning (EL) models were utilized to establish nonlinear connections between the semantic and object features of streetscapes and CO2 concentrations. After performing EL fusion modeling, the predictive R2 in the test set exceeded 90 %, and the mean absolute error (MAE) was <3.2 ppm. The model was applied to Baidu Street View Panoramas (BSVPs) in Shenzhen to generate a map of average on-road CO2 with a 100 m resolution, and the Local Indicator of Spatial Association (LISA) was then used to identify high CO2 intensity spatial clusters. Additionally, the Light Gradient Boost-SHapley Additive exPlanation (LGB-SHAP) analysis revealed that vertically planted trees can reduce CO2 emissions from on-road sources. Moreover, the factors that affect on-road CO2 exhibit interaction and threshold effects. Street View Panoramas (SVPs) and Artificial Intelligence (AI) were adopted here to enhance the spatial measurement of on-road CO2 concentrations and the understanding of driving factors. Our approach facilitates the assessment and design of low-emission transportation in urban areas, which is critical for promoting sustainable traffic development.

Spatial-temporal patterns and influencing factors of the Building Green View Index: A new approach for quantifying 3D urban greenery visibility

The vertical expansion of urbanization has increased the morphological heterogeneity of the urban landscape, affecting the physical and emotional wellbeing of urban dwellers by obstructing the view of greenery. In this study, multisource spatial data was used to calculate the Building Green View Index (BGVI). Baidu Street View (BSV) images were collected for comparison with the corresponding BGVI results. A random forest model was used to analyze the contributions and marginal effects of multiple influencing factors on BGVI. The results indicated that approximately 76.10 % of the sampled sites had a higher BGVI than the street view green view index, indicating buildings' superiority of visible greenery in height. The western edge of the research region frequently had the highest BGVI. Meanwhile, the hotspot regions were primarily located in the west, which was more consistent with the distribution of high value zones. The green area within the maximum visible distance, the maximum visible distance, and the average height were the most influential factors of BGVI according to marginal effects analysis with the highest IncNodePurity. As a quantitative measure of urban dwellers’ visual accessibility to green space, the BGVI will contribute to urban green planning and the development of landscape architecture.

Tree abundance, species richness, or species mix? Exploring the relationship between features of urban street trees and pedestrian volume in Jinan, China

Accumulating evidence has confirmed that urban greenery, especially street trees, is beneficial to walking behaviors. Existing street greenery exposure-walking behavior studies have focused on the quantity of greenery, which is often measured by normalized difference vegetation index (NDVI) or green view index (GVI). However, some important qualities of street trees, the dominant component of urban greenery, were often overlooked, due to the labor-intensive and expensive nature of on-site surveys. To address this issue, we proposed a cutting-edge deep learning technique to identify street tree species at the individual tree level. We established a citywide dataset of all street trees with species information via 185,831 Baidu Street View images (BSV) in Jinan, China. Population-level walking intensity, measured by pedestrian volume, was retrieved from BSV images using Baidu AI. We further adopted spatial regression models to investigate the association between pedestrian volume and street tree characteristics, including street tree abundance (number of street trees), species richness (number of unique tree species) and species mix (the degree of diversity of tree species). The built environment and urban greenery covariates were adjusted in the models. The results indicate that the street tree abundance and species mix are positively associated with pedestrian volume. Species richness is not associated with it. Besides, spatial mismatch is identified between abundance and species mix of street trees in the study area. Hence, to facilitate walking behavior and deliver related health benefits, it is necessary to develop fine-grained measures of street greenery features.

Socioecological justice in urban street greenery based on green view index-A case study within the Fuzhou Third Ring Road

Urban green space equity relates to the efficient allocation of natural resources and the equalization of public service facilities. Street (road) greenery provides substantial ecological, social and cultural benefits. Thus, in this study, a subdistrict-level evaluation framework for the fairness of the spatial distribution of street greenery was proposed, taking a case study within the Third Ring Road of Fuzhou City in Fujian, China. Street view images may capture the green information in a vertical dimension for the indirect representation of people’s perspective on the ground. The green view index, which was estimated based on Baidu Street View images, was employed to represent the urban street greenery, and the results were combined using deep learning technology. The Gini coefficient, share index and location entropy were used as evaluation indicators for the fairness of the spatial distribution of the street green view index. Furthermore, this framework combined socioeconomic data and population census data to explore the correlation among socioeconomic status, age, and evaluation index at the subdistrict level. In addition, we analyzed street greenery distribution inequalities from the perspective of socioecological justice. The results showed that in Fuzhou, there is a significant correlation among the Gini coefficient, green view index and socioeconomic status. In addition, subdistricts with a lower green view index have a less equitable street greenery distribution, people with low socioeconomic status may suffer from green injustice, and seniors have a lower accessibility to street green space than people with the average social status. Our analytical approach is applicable for other cities, and the findings are useful for greenery spatial planning processes and evaluating construction effects.

Application of an Integrated Model for Analyzing Street Greenery through Image Semantic Segmentation and Accessibility: A Case Study of Nanjing City

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

Integrating restorative perception into urban street planning: A framework using street view images, deep learning, and space syntax

Existing urban street planning often neglects human perception despite being designed for human utility. Therefore, exploring the relationship between urban street planning and human perception, especially restorative perception, is crucial for sustainable urban planning. In this study, we analyzed restorative perception of urban streets among different population groups and accessibility of urban streets using Baidu Street View Images, deep learning, and space syntax. Furthermore, we clarified the impacts of various street elements on restorative perception using correlation and ridge regression analyses. Based on restorative perception and accessibility coupling assessment, streets were classified into four types, identifying “inefficient segments” and “prioritized segments”. The results showed that: 1) the level of restorative perception of streets in Nanjing was relatively limited and exhibited variation across different population groups; 2) the impacts of different street elements on restorative perception varied, with trees having the highest positive impact (β = 0.378), while walls had the greatest negative impact (β = − 0.182); and 3) “prioritized segments” and “inefficient segments” represented 33.92 % and 17.96 %, respectively, indicating the urgent demand for streets planning and renewal. These findings can offer targeted recommendations for enhancing restorative environment of urban streets and identifying priority areas for urban street planning.

Spatial differentiation characteristics and influencing factors of the green view index in urban areas based on street view images: A case study of Futian District, Shenzhen, China

Urban green space provides a variety of benefits, and the green view index (GVI) is regarded as an effective indicator to evaluate the quality of green spaces from a human perspective. To investigate the drivers of the spatial differentiation in GVI in urban areas, the GVI was calculated based on Baidu street view images and semantic segmentation in Futian District, Shenzhen, and its spatial variation characteristics were explored. The dominant influencing factors of GVI were analyzed and screened by applying the Pearson correlation coefficient and optimal parameters-based geographical detector (OPGD). The results showed that the overall GVI was high in the study area, with an average value of approximately 28%. The scale effect of GVI was not obvious, but the spatial heterogeneity was distinct, and the hot spots and cold spots of GVI had an evident clustered distribution. The spatial variation of GVI was influenced by the vegetation cover, landscape pattern, built environment and socioeconomic factors. The normalized difference vegetation index (NDVI) was the most dominant factor affecting the spatial differentiation of GVI, followed by land cover, fractal dimension index, patch area, building density and proximity. Furthermore, the interaction of the influencing factors had a higher degree of explanation than a single factor. In highly urbanized areas, exploring the factors affecting the spatial differentiation of GVI can provide a basis for optimizing the layout and structure of green spaces, contributing to a better perception and quality of urban greening.

Characterizing street trees in three metropolises of central China by using Street View data: From individual trees to landscape mapping

Street trees can benefit urban spaces, and the characterization of them is a basis for evaluating their functions and proper management. In this paper, we speculate that Street View imagery-based measurement favors characterizing street trees from individual tree sizes and growth status to spatial mapping and risk region identification at multiple city levels. Using Wuhan, Changsha, and Nanchang as examples, nearly 6000 sites and over 111,000 Baidu Street View (BSV) images were used to measure tree sizes, growth status, and vertical structure. Together with BSV-derived tree data, we also calculated the street-view greenness index (GVI) and the Normalized Difference Vegetation Index (NDVI) from remote sensing data, and then city-level spatial mapping was produced to identify risk regions for urban afforestation. The results showed that BSV measurement could compare city-level street trees, i.e., Changsha had a 1.2–1.3-fold higher diameter, tree height, and canopy size than the lowest at Nanchang; Changsha had the highest GVI, but the lowest NDVI; while Nanchang had the lowest GVI, but the highest NDVI, showing different significance between street-view and remote-sensing greenness. BSV data could identify urbanization effects, i.e., the tree sizes decreased from the urban center to the edge, and three cities showed the same pattern. By using BSV data, the growth status score was summarized from leaf blade color, tree-supported, dieback, and dead-tree percents, and thereafter, urbanization-induced decreases of growth status scores can be observed from the city center to the suburbs. At the same urban-rural gradient, BSV data found the vertical structure tended to be complex, and the sky and middle GVI decreased while the ground GVI rose in all three cities in general. The BSV data made landscape mapping available, and the map can be used to identify risk areas of big-sized tree conservation, health problems, greenness deficiency, and the over-simplified forest structure within a city or among different cities. Together with BSV-derived street development data, complex associations between tree characteristics and geo-climates and street social development could be decoupled by redundancy ordination. In general, streets with more pedestrians were often accompanied by taller, larger, healthier trees, more complex communities, and an increasing sky/ground ratio of GVI greenery. Our findings highlighted that street-view data could be used to assess street tree characteristics, geographic distribution, and possible associations with other factors, favoring the city-level urban tree inventory from individuals to the landscape for urban tree management and policymaking.

Measuring the Multiple Functions and Tradeoffs among Streets: A New Framework Using the Deep Learning Method

With the sustainable and coordinated development of cities, the formulation of urban street policies requires multiangle analysis. In regard to the existing street research, a large number of studies have focused on specific landscapes or accessibility of streets, and there is a lack of research on the multiple functions of streets. Recent advances in sensor technology and digitization have produced a wealth of data and methods. Thus, we may comprehensively understand streets in a less labor-intensive way, not just single street functions. This paper defines an index system of the multiple functions of urban streets and proposes a framework for multifunctional street measurement. Via the application of deep learning to Baidu Street View (BSV) imagery, we generate three functions, namely, landscape, traffic, and economic functions. The results indicate that street facilities and features are suitably identified. According to the multifunctional perspective, this paper further classifies urban streets into multifunctional categories and provides targeted policy recommendations for urban street planning. There exist correlations among the various street functions, and the correlation between the street landscape and economic functions is highly significant. This framework can be widely applied in other countries and cities to better understand street differences in various cities.

Research on Street Color Environment Perception Based on CEP-KASS Framework

The color of urban streets plays a crucial role in shaping a city’s image, enhancing street appeal, and optimizing the experience of citizens. Nevertheless, the relationship between street color environment and residents’ perceptions has rarely been deeply discussed, and most of the existing studies adopt qualitative methods. To accurately and effectively assess the connection between street color environment and residents’ emotional perceptions, this paper introduces a quantitative research framework based on multi-source data called “Color Emotion Perception with K-Means, Adversarial Strategy, SegNet, and SVM (CEP-KASS)”. By combining K-Means unsupervised machine learning and SegNet computer vision techniques, it captures and analyzes visual elements and color data from Baidu Street View Images (BSVI). It then employs a human–machine adversarial scoring model to quantify residents’ perceptions of BSVI and uses the support vector machine regression model to predict the final perception scores. Based on these data, a Pearson correlation analysis and visual analysis were conducted on the elements and color in the urban environment. Subsequently, the streets were classified based on perception frequency and perception scores by integrating multi-source data, and areas within the third ring of Xuzhou City were selected for validating the research framework. The results demonstrate that utilizing street-view images and the CEP-KASS framework can quantitatively analyze urban color perception and establish a connection with residents’ emotions. In terms of color perception, red, orange, and blue all have a strong positive correlation with the interesting score, whereas black is positively correlated with a sense of safety. Regarding color attributes, low-saturation bright colors result in higher fun perception scores in urban spaces; too low saturation and brightness can affect their attractiveness to residents; brightness has an inverse relationship with the perception of safety, and adjusting brightness inversely can improve the perceived safety experience in certain urban external spaces. The street classification criteria based on perception frequency and perception scores proposed herein can provide references for planners to prioritize color transformation decisions, with a priority on emulating HSHF streets and transforming LSHF streets. When formulating color planning, suggestions for color adjustment can be given based on the correlation study of color with visual elements and perception scores, optimizing urban residents’ spatial perception and their emotional experiences. These findings provide robust theoretical support for further enhancing the visual quality of streets and refining urban color planning.

How does three-dimensional landscape pattern affect urban residents' sentiments

The impact of urban street landscapes on residents' sentiments is a critical concern. However, the current representation of street landscapes through landscape pattern two-dimensional metrics (LP2DM) derived from remote sensing images neglects the perceptibility of residents' visible environments at the eye level. To address this gap, we developed a novel landscape pattern three-dimensional metric (LP3DM) to quantitatively represent landscape perceptibility based on four individual perception dimensions: green space, gray space, openness, and crowding. We then investigated the relationships between LP3DM and residents' sentiments using Baidu street view images and Weibo social media textual big data in Beijing, China. Our results demonstrate that LP3DM is more significant correlated with residents' sentiments than LP2DM (average contribution, ACLP2DM=0.025, ACLP3DM=0.054). Notably, the greenness metric exhibited the highest contribution (AC=0.12), with the greenness three-dimensional metric showing a positive correlation (r = 0.15, p < 0.01) with residents' sentiments, while grayness exhibited a slightly negative correlation (r = −0.087, p < 0.1). Our study highlights the importance of considering the perceptibility of natural landscape elements in addition to their quantity during urban construction to enhance residents' sentimental well-being. Overall, our LP3DM framework offers a promising approach to capture residents' landscape perceptibility and inform urban planning and design decisions.

Integrating street view images and deep learning to explore the association between human perceptions of the built environment and cardiovascular disease in older adults

Understanding how built environment attributes affect health remains important. While many studies have explored the objective characteristics of built environments that affect health outcomes, few have examined the role of human perceptions of built environments on physical health. Baidu Street View images and computer vision technological advances have helped researchers overcome the constraints of traditional methods of measuring human perceptions (e.g., these methods are laborious, time-consuming, and costly), allowing for large-scale measurements of human perceptions. This study estimated human perceptions of the built environment (e.g., beauty, boredom, depression, safety, vitality, and wealth) by adopting Baidu Street View images and deep learning algorithms. Negative binomial regression models were employed to analyze the relationship between human perceptions and cardiovascular disease in older adults (e.g., ischemic heart disease and cerebrovascular disease). The results indicated that wealth perception is negatively related to the risk of cardiovascular disease. However, depression and vitality perceptions are positively associated with the risk of cardiovascular disease. Furthermore, we found no relationship between beauty, boredom, safety perceptions, and the risk of cardiovascular disease. Our findings highlight the importance of human perceptions in the development of healthy city planning and facilitate a comprehensive understanding of the relationship between built environment characteristics and health outcomes in older adults. They also demonstrate that street view images have the potential to provide insights into this complicated issue, assisting in the formulation of refined interventions and health policies.

Exploring non-linear and synergistic effects of green spaces on active travel using crowdsourced data and interpretable machine learning

The relationship between green spaces and active travel has been extensively studied. However, the majority of previous studies relied on small datasets concerning active travel and inadequately explored non-linear and/or synergistic effects. This study uses multi-source data and interpretable machine learning techniques to identify the non-linear and synergistic effects of green spaces in Chengdu (China) on two types of active travel: cycling and running. Crowdsourced data from Strava collected in December 2021 is used to measure city-wide active travel levels. Meanwhile, green spaces are evaluated from two viewpoints: overhead view and eye level, with the latter assessed using Baidu Street View imagery. The findings demonstrate that green spaces can account for up to 20% of the variance in active travel. Generally, the effect of the area of green spaces on active travel is positive. When the area of green spaces reaches a certain threshold, its effect becomes marginal and even negative. The green view index displays complex effects on cycling. Furthermore, this study identifies synergistic effects among predictors (e.g., green view index & river line length).

Understanding the nonlinear effects of the street canyon characteristics on human perceptions with street view images

Human perceptions represent the psychological experiences and feelings of individuals toward the surrounding environment. It is influenced by various physical elements (e.g., sky, tree, building, etc.) within the street canyon. However, prior research has predominantly relied on field surveys, high-cost methods, and restricted data sources, thereby limiting analyses of the impact of street canyon characteristics on human perceptions. Importantly, the nonlinear effects of street canyon characteristics on human perceptions have not been fully understood by existing studies. Thus, this study employed Baidu Street View images to evaluate street canyon characteristics and human perceptions at a visual level. A random forest regression was then utilized to uncover the nonlinear effects of street canyon characteristics on human perceptions. The results suggested that some street canyon characteristics nonlinearly affected human perceptions. Specifically, the optimal sky proportion within the individual visual field was 15%, and the appropriate thresholds for trees and grass were 17.5% and 1.0%, respectively. Overall, positive perceptions might be increased if the proportion of natural characteristics within street canyons was maintained below the optimal threshold. In addition, threshold effects have been observed in relation to the perception of beauty due to cars, the perception of vitality and security due to roads, and the perception of boredom due to walls. The findings of this study can serve as scientific evidence and inform urban renewal based on a people-oriented approach.

Alternative scenarios for urban tree surveys: Investigating the species, structures, and diversities of street trees using street view imagery

Grasping information about street trees can assist urban environmental managers in quantifying and evaluating their costs and ecological benefits. Street view imagery has the potential for urban street tree surveys. However, few studies have been conducted on the inventory of street tree species, size structures and diversity based on street view imagery at the urban scale. In this study, we tried to conduct a survey of street trees in urban areas of Hangzhou using street view images. First, we constructed a size reference items system and determined that using it for street view measurements of street trees was comparable to field measurements results (R2 = 0.913–0.987). On this basis, we investigated the distribution characteristics and differences of street trees in Hangzhou using Baidu Street View and found that Cinnamomum camphora was the dominant tree species in Hangzhou (46.58 %), and the high proportion made urban street trees susceptible to ecological hazards. In addition, surveys conducted separately in various urban districts revealed that the diversity of street trees in new urban areas was smaller and less uniform. Additionally, as the gradient got further away from the city center, the street trees are smaller, the diversity first increased and then decreased, and the evenness gradually decreased. This study analyzes the use of Street View to investigate the distribution of species, size structure, and diversity of urban street trees. The use of street view imagery will simplify the collection of data on urban street trees and provide urban environmental managers with a foundation for strategy development.

Assessing the Spatial Diversity of Urban Cultural Landscape using Street View Data and Investigating Influential Factors: A Case Study of Beijing

As an open space bearing the urban context, urban cultural landscape is an important place to enhance the connotation of urban space and create the vitality of urban space. Cultural landscape is influenced by multiple factors in the process of formation and evolution, and there are distinct differences in individuation and regionalization. In this paper, with the help of Baidu Street View images, the evaluation system and measurement method are constructed from the five dimensions of architectural style, infrastructure, ecological environment, urban sketch and historical context. Taking Beijing as an example, the man-machine combination method is adopted to explore the factors affecting the spatial heterogeneity of urban cultural landscape based on GIS grid, and effective remediation measures are proposed. It has positive significance to enhance the connotation of urban culture, strengthen the comfort level of citizens and promote the deep development of urban cultural space.

Building Façade Color Distribution, Color Harmony and Diversity in Relation to Street Functions: Using Street View Images and Deep Learning

Building façade colors play an important role in influencing urban imageability, attraction and citizens’ experience. However, the relations between street functions and the building façade color distribution, color harmony and color diversity have not been thoroughly examined. We obtained the dominant colors of building façades in Changning District, Shanghai, utilizing Baidu street view images, image semantic segmentation technology and the K-means algorithm. The variations in building façades’ dominant colors, color harmony and diversity across different types of functional streets were examined through logistic regression and ANOVA analyses. The results indicate that, compared to industrial streets, red hues are more common in science education streets, residential streets and mixed functional streets. Business streets are more likely to have hues of green, red and red–purple. Residential streets’ saturation is overall higher than that of industrial streets. In business streets, the medium–high value occurs less frequently than other streets. Moreover, we found that the street building façade colors in industrial streets were more harmonious and less diversified than that in other functional streets. This study has implications for urban color planning practices. Color harmony and color diversity should be well considered in future planning. The role of street functions should also be addressed in building façade color planning, to improve existing planning frameworks as well as related strategies.

Monitoring and mapping vegetation greenery in residential communities using street view images and a Normalized Vegetation Greenery Index: a case study in Beijing, China

Vegetation greenery is essential for the sensory and psychological wellbeing of residents in residential communities. To enhance the quality of regulations and policies to improve people’s living environments, it is crucial to effectively identify and monitor vegetation greenery from the perspective of the residents using effective images and methods. In this study, Baidu street view (BSV) images and a Normalized Vegetation Greenery Index (NVGI) based method were examined to distinguish vegetation greenery in residential communities of Beijing, China. The magnitude of the vegetation was quantified and graded, and spatial analysis techniques were employed to investigate the spatial characteristics of vegetation greenery. The results demonstrated that (1) the identified vegetation greenery using the proposed NVGI-based method was closely correlated with those of the reference classification (r = 0.993, p = 0.000), surpassing the comparison results from the SVM method, a conventional remote sensing classification means; (2) the vegetation greenery was distributed unevenly in residential communities and can be categorized into four grades, 63.79% of the sampling sites were found with relatively low (Grade II) and moderate (Grade III) vegetation greenery distribution, most of the districts in the study area contained zero-value green view index sites; and (3) there was significant spatial heterogeneity observed in the study area, with low-value clustering (cold spots) predominantly located in the central region and high-value clustering (hot spots) primarily concentrated in the peripheral zone. The findings of this study can be applied in other cities and countries that have street view images available to investigate greenery patterns within residential areas, which can help improve the planning and managing efforts in urban communities.

Feasibility of urban bird occurrence and nest amount evaluation by the street-view image virtual survey.

Bird observation mainly relies on field surveys, which are time-consuming and laborious. In this study, we explored using street-view images in the virtual survey of urban birds and nests. Using the coastal city of Qingdao as the study area, 47 201 seamless spherical photos at 2741 sites were collected using the Baidu street-view (BSV) map. Single-rater-all photo checks and seven-rater-metapopulation checks were used to find inter-rater repeatability, the best viewing layer for BSV collection, and possible environments affecting the results. We also collected community science data for comparison. The BSV time machine was used to assess the temporal dynamics. Kappa square test, generalized linear model, redundancy ordination and ArcMap were used in the analysis. Different rater repeatability was 79.1% in nest evaluations and 46.9% in bird occurrence. A re-check of the different-rating photos can increase them to 92% and 70%. Seven-rater statistics showed that more than 5% sampling ratio could produce a non-significant different bird and nest percentage of the whole data, and the higher sampling ratio could reduce the variation. The middle-viewing layer survey alone could produce 93% precision of the nest checks by saving 2/3 of the time used; in birds, selecting middle and upper-view photos could find 97% of bird occurrences. In the spatial distribution, the nest's hotspot areas from this method were much greater than the community science bird-watching sites. The BSV time machine made it possible to re-check nests in the same sites but challenging the re-check of bird occurrences. The nests and birds can be observed more in the leafless season, on wide, traffic-dense coastal streets with complex vertical structures of trees, and in the gaps of tall buildings dominated by road forests. Our results indicate that BSV photos could be used to virtually evaluate bird occurrence and nests from their numbers, spatial distribution and temporal dynamics. This method provides a pre-experimental and informative supplement to large-scale bird occurrence and nest abundance surveys in urban environments.