Google Street View Images Research Articles

Examining the Change of Green View Index (GVI) at Street Eye-Level with GSV and YSV

Green spaces in cities make them look better and benefit the environment and people's well-being. Spending time in nature reduces stress, improves mental relaxation, and increases happiness. Green spaces also encourage people to be more active, which strengthens muscles, improves heart health, and reduces the risk of obesity. Tools like Google Street View (GSV) and Yandex Street View (YSV) simplify tracking and assessing these urban green areas. This gives planners up-to-date information to effectively plan and manage cityscapes. However, limitations exist. Information on street-level greenery assessment can be scarce, making planning challenging. This study used the Green View Index (GVI) to assess street greenery using GSV images. The study found GSV and YSV to be good tools for assessing and monitoring street-level greenery. GVI was also suggested as a possible way to objectively measure street-level greenness, which could be useful for urban landscape planning and management.

What Characteristics Modify the Relation of Neighborhood Walkability and Walking Behavior in Older Adults?

Neighborhood walkability can influence walking behaviors in older adults. However, its associations of walkability with walking may differ by demographic, socioeconomic, health, social, and residential characteristics due to factors such as increased vulnerability to environmental factors or increased resilience. In a sample of older adults (n = 493, median age = 82 [range 78-89], 56% female, 31% Black), a walkability index was derived from audits of Google Street View images of participants' immediate neighborhoods. Walking was self-reported in the past week. Effect modifiers by demographic (age, race, gender, marital status), socioeconomic (education, income), health (gait speed, falls, knee pain, depressive symptoms, cognitive status, perceived energy), social (driving status, social engagement), and residential/neighborhood (residence type, neighborhood socioeconomic status [SES], population density) characteristics were assessed by interaction terms in logistic regression models, adjusted for gender, race, gait speed, prior falls, high depressive symptoms, currently driving, and cognitive status. When effect modification was suggested (p for interaction <.1), adjusted analyses of walkability with walking stratified on the effect modifier were conducted. In this sample, 59% walked in the past week and greater walkability was associated with greater odds of walking (odds ratio [OR] = 1.16, 95% confidence interval [CI]: 1.01-1.34). This association (p for interaction range 0.002-0.07) was present for those who were not currently married (OR = 1.35, 95% CI: 1.17-1.56), who reported knee pain (OR = 1.40, 95% CI: 1.14-1.72) or high depressive symptoms (OR = 1.30, 95% CI: 1.06-1.60), or who had declining cognitive function (OR = 1.30, 95% CI: 1.09-1.55). High walkability may influence physical activity, particularly for those with vulnerabilities related to social, pain, and brain health characteristics. These results should inform neighborhood planning and targeted interventions for vulnerable older adults.

Virtual Assessment of Physical Activity-Related Built Environment in Soweto, South Africa: What Is the Role of Contextual Familiarity?

Understanding how urban environments shape physical activity is critical in rapidly urbanizing countries such as South Africa. We assessed the reliability of virtual audits for characterizing urban features related to physical activity in Soweto, South Africa. We used the Microscale Audit of Pedestrian Streetscapes Global tool to characterize pedestrian-related features from Google Street View images in four neighborhoods of Soweto. Neighborhoods were selected to represent different levels of deprivation. Inter-rater reliability was analyzed according to the rater's familiarity with the local area. The results show a higher inter-rater reliability was observed among auditors with greater contextual familiarity. Many measurements however generated inconclusive results due to either low variability in the raters' responses or the absence of the features in the streets. It is evident from our findings that virtual audits are efficient tools that can be used to assess the built environment. However, to ensure meaningful use of these tools in diverse settings, we recommend that auditors comprise of people with contextual familiarity.

Understanding urban traffic flows in response to COVID-19 pandemic with emerging urban big data in Glasgow

Urban traffic analysis has played an important role in urban development, providing insights for urban planning, traffic management, and resource allocation. Meanwhile, the global pandemic of COVID-19 has significantly changed people's travel behaviour in urban areas. This research uses the spatial Durbin model to understand the relationship between traffic flows, urban infrastructure, and socio-demographic indicators before, during, and after pandemic periods. We include factors such as road characteristics, socio-demographics, surrounding built environments (land use and nearby points of interest), and the emerging urban big data source of Google Street View images to understand their influences on time series traffic flows. Taking the city of Glasgow as the case study, we have found that areas with more young and white dwellers are associated with more traffic flows, while natural green spaces are associated with fewer traffic flows. Major roads between cities and towns also show heavier traffic flows. Besides, the application of Google Street View images in this research has revealed the heterogeneous effects of green space on urban traffic flows, as the magnitudes of their effects vary by distance. We also detect that the spatial dependence between adjacent neighbourhoods among the traffic flows and associated urban parameters is variable during the four COVID-19 periods. With the influence of COVID-19, there has been a significant decrease in long-distance travel. The noticeable change in travel behaviour presents a valuable opportunity to encourage active travel in the near future.

Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019–2021 fatal collisions in the USA

ObjectiveThe USA has higher rates of fatal motor vehicle collisions than most high-income countries. Previous studies examining the role of the built environment were generally limited to small geographic areas...

Fusion of Google Street View, LiDAR, and Orthophoto Classifications Using Ranking Classes Based on F1 Score for Building Land-Use Type Detection

Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this data. Earth observation data can be beneficial in this regard, because of their availability and requiring a reduced amount of fieldwork. In this work, we imported Google Street View (GSV), light detection and ranging-derived (LiDAR-derived) features, and orthophoto images to deep learning (DL) models. The DL models were trained on building land-use type data for the Greater Toronto Area (GTA). The data was created using building land-use type labels from OpenStreetMap (OSM) and web scraping. Then, we classified buildings into apartment, house, industrial, institutional, mixed residential/commercial, office building, retail, and other. Three DL-derived classification maps from GSV, LiDAR, and orthophoto images were combined at the decision level using the proposed ranking classes based on the F1 score method. For comparison, the classifiers were combined using fuzzy fusion as well. The results of two independent case studies, Vancouver and Fort Worth, showed that the proposed fusion method could achieve an overall accuracy of 75%, up to 8% higher than the previous study using CNNs and the same ground truth data. Also, the results showed that while mixed residential/commercial buildings were correctly detected using GSV images, the DL models confused many houses in the GTA with mixed residential/commercial because of their similar appearance in GSV images.

Occupation Prediction with Multimodal Learning from Tweet Messages and Google Street View Images

Occupation Prediction with Multimodal Learning from Tweet Messages and Google Street View Images

Associations of street-view greenspace with Parkinson’s disease hospitalizations in an open cohort of elderly US Medicare beneficiaries

IntroductionProtective associations of greenspace with Parkinson’s disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). MethodsWe created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007–2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. ResultsThere were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. ConclusionIncreasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic.

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed.

Impacts of Streetscape Features on Individual Social Capital: Applying Korea’s Neighborhood Data to Street View Images to Improve Lives of the Socially Vulnerable

Neighborhood social vulnerability is a dimension of vulnerability that influences society’s response to external factors, encompassing various social factors tied to socioeconomic and demographic attributes at the neighborhood level. While previous studies have explored the associations between vulnerable neighborhoods and stressors like environmental hazards, understanding of their impact on residents’ social capital remains limited. Moreover, the role of built environments in mitigating these effects is uncertain. This study investigates the impact of neighborhood social vulnerability, alongside streetscape features, on individual social capital in Seoul, South Korea. Using a stratified random sampling method, we surveyed 1000 Seoul residents, assessing their social capital and demographics. In particular, six questionnaires based on a four-point Likert scale were used to measure the individual level of social capital: two questions for social networks, three questions for social participation, and one question for sense of community. Additionally, we employed a deep learning-based model to analyze Google Street View images for detailed streetscape features. Our analysis, utilizing multiple and ordinal logistic regression models, reveals that walkable environments and neighborhood prosperity, indicated by factors like income, education, and street greenery, are linked to higher social capital. Moreover, less urbanized areas with lower land prices and greater openness to the sky also show positive associations. These findings underscore the potential of improving streetscape features to mitigate the negative impacts of neighborhood vulnerability, particularly in neighborhoods where low-income and less-educated residents are concentrated, offering insights for urban planners and community leaders.

ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images

We propose an automated lowest floor elevation (LFE) estimation algorithm based on computer vision techniques to leverage the latent information in street view images. Flood depth-damage models use a combination of LFE and flood depth for determining flood risk and extent of damage to properties. We used image segmentation for detecting door bottoms and roadside edges from Google Street View images. The characteristic of equirectangular projection with constant spacing representation of horizontal and vertical angles allows extraction of the pitch angle from the camera to the door bottom. The depth from the camera to the door bottom was obtained from the depthmap paired with the Google Street View image. LFEs were calculated from the pitch angle and the depth. The testbed for application of the proposed method is Meyerland (Harris County, Texas). The results show that the proposed method achieved mean absolute error of 0.190 m (1.18 %) in estimating LFE. The height difference between the street and the lowest floor (HDSL) was estimated to provide information for flood damage estimation. The proposed automatic LFE estimation algorithm using street view images and image segmentation provides a rapid and cost-effective method for LFE estimation compared with the surveys using total station theodolite and unmanned aerial systems. By obtaining more accurate and up-to-date LFE data using the proposed method, city planners, emergency planners and insurance companies could make a more precise estimation of flood damage.

Neighborhood built environment, obesity, and diabetes: A Utah siblings study

BackgroundThis study utilizes innovative computer vision methods alongside Google Street View images to characterize neighborhood built environments across Utah. MethodsConvolutional Neural Networks were used to create indicators of street greenness, crosswalks, and building type on 1.4 million Google Street View images. The demographic and medical profiles of Utah residents came from the Utah Population Database (UPDB). We implemented hierarchical linear models with individuals nested within zip codes to estimate associations between neighborhood built environment features and individual-level obesity and diabetes, controlling for individual- and zip code-level characteristics (n = 1,899,175 adults living in Utah in 2015). Sibling random effects models were implemented to account for shared family attributes among siblings (n = 972,150) and twins (n = 14,122). ResultsConsistent with prior neighborhood research, the variance partition coefficients (VPC) of our unadjusted models nesting individuals within zip codes were relatively small (0.5%–5.3%), except for HbA1c (VPC = 23%), suggesting a small percentage of the outcome variance is at the zip code-level. However, proportional change in variance (PCV) attributable to zip codes after the inclusion of neighborhood built environment variables and covariates ranged between 11% and 67%, suggesting that these characteristics account for a substantial portion of the zip code-level effects. Non-single-family homes (indicator of mixed land use), sidewalks (indicator of walkability), and green streets (indicator of neighborhood aesthetics) were associated with reduced diabetes and obesity. Zip codes in the third tertile for non-single-family homes were associated with a 15% reduction (PR: 0.85; 95% CI: 0.79, 0.91) in obesity and a 20% reduction (PR: 0.80; 95% CI: 0.70, 0.91) in diabetes. This tertile was also associated with a BMI reduction of −0.68 kg/m2 (95% CI: −0.95, −0.40) ConclusionWe observe associations between neighborhood characteristics and chronic diseases, accounting for biological, social, and cultural factors shared among siblings in this large population-based study.

Artificial intelligence-based assessment of built environment from Google Street View and coronary artery disease prevalence.

Built environment plays an important role in the development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches have been limited. This study aimed to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in US cities. This cross-sectional study used features extracted from Google Street View (GSV) images to measure the built environment and link them with prevalence of coronary heart disease (CHD). Convolutional neural networks, linear mixed-effects models, and activation maps were utilized to predict health outcomes and identify feature associations with CHD at the census tract level. The study obtained 0.53 million GSV images covering 789 census tracts in seven US cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO). Built environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The addition of GSV features improved a model that only included census tract-level age, sex, race, income, and education or composite indices of social determinant of health. Activation maps from the features revealed a set of neighbourhood features represented by buildings and roads associated with CHD prevalence. In this cross-sectional study, the prevalence of CHD was associated with built environment factors derived from GSV through deep learning analysis, independent of census tract demographics. Machine vision-enabled assessment of the built environment could potentially offer a more precise approach to identify at-risk neighbourhoods, thereby providing an efficient avenue to address and reduce cardiovascular health disparities in urban environments.

Integrating visual factors in crash rate analysis at Intersections: An AutoML and SHAP approach towards cycling safety

Cycling crashes constitute a significant and rising share of traffic accidents. Consequently, exploring factors affecting cycling safety has become a priority for both governmental bodies and scholars. However, most existing studies have neglected the vision factors capable of quantitatively describing the city-level cycling environment. Moreover, they have relied on limited models that lack interpretability and fail to capture the spatial variations in the contribution of factors. To address these gaps, this research proposed a framework that used origin–destination-based cycling flow and vision factors generated from Google Street View images to identify the leading factors. It also employed the comparative Automatic Machine Learning and interpretable SHAP value-based geospatial analysis to explain each factor’s contribution to the cycling crash risk, with a particular focus on the spatial variations in the influence of vision factors. The effectiveness of this framework was validated by a case study in Manhattan, which examined the leading risk factors of cycling crash rates at intersections. The results showed that the LightGBM model, with selected subsets of factors, outperformed other models. Through SHAP explanations of global feature importance, the study identified the proportion of road barriers, the proportion of open sky, and the number of visible trucks as the leading visual risk factors. Additionally, using SHAP-based geospatial analysis, the study revealed the local variations in the effects of these three factors and identified eight areas with higher cycling crash rates. Based on these findings, the study provided practical measures for a safer cycling environment in Manhattan.

Associations between community green view index and fine particulate matter from Airboxes

Urban greenery can help to improve air quality, reduce health risks and create healthy livable urban communities. This study aimed to explore the role of urban greenery in reducing air pollution at the community level in Tainan City, Taiwan, using air quality sensors and street-view imagery. We also collected the number of road trees around each air quality sensor site and identified the species that were best at absorbing PM2.5. Three greenness metrics were used to assess community greenery in this study: two Normalized Difference Vegetation Indices (NDVI) from different satellites and the Green View Index (GVI) from Google Street View (GSV) images. Land-use Regression (LUR) was used for statistical analysis. The results showed that a higher GVI within a 500 m buffer was significantly associated with decreased PM2.5. Neither NDVI metrics within a 500 m circular buffer were significantly associated with decreased PM2.5. Evergreen trees were significantly associated with lower ambient PM2.5, compared with deciduous and semi-deciduous trees. Because localized changes in air quality profoundly affect public health and environmental equity, our findings provide evidence for future urban community greenspace planning and its beneficial impacts on reducing air pollution.

Building typology classification using convolutional neural networks utilizing multiple ground-level image process for city-scale rapid seismic vulnerability assessment

Several studies have focused on generating seismic vulnerability maps for earthquake-prone areas, particularly in Indonesia. Building typologies are a key factor in determining vulnerability to earthquakes. However, conducting large-scale field surveys to determine the spatial distribution of building typologies in a city is uneconomical. This paper explores the use of a convolutional neural network (CNN) to automatically detect building typologies from diverse regions in Indonesia, utilizing both conventional and automated building image acquisition processes. In this study, datasets from three distinct image acquisition methods are trained with four unique CNN architectures to identify the best-performing model to classify building typologies. The sample size effect on CNN performance is also investigated. The results showed that randomly sampled Google Street View (GSV) images are the most effective dataset for the CNN model, achieving an f1-score of 84.33%. Among the network architectures tested, MobileNet demonstrated superior performance on the majority of evaluated datasets. As the sample size increases by about 350% in the dataset, there is a positive correlation with up to 2.3% f1-score improvement. Using the best-performing CNN model, two building vulnerability models were employed to assess the spatial distribution of building damage in the urban area of Bandung, considering a hypothetical scenario of an M7 earthquake. Incorporating local construction data, one of the generated maps estimated that approximately 55% of buildings in Bandung would experience moderate to severe structural damage. This study showcases the potential of CNN models in automating regional seismic assessments and providing valuable insights for comprehensive seismic mitigation strategies.

Predicting college retention rates from Google Street View images of campuses

Every year, around 40% of undergraduate students in the United States discontinue their studies, resulting in a loss of valuable education for students and a loss of money for colleges. Even so, colleges across the nation struggle to discover the underlying causes of these high dropout rates. In this paper, the authors discuss the use of machine learning to find correlations between the built environment factors and the retention rates of colleges. They hypothesized that one way for colleges to improve their retention rates could be to improve the physical characteristics of their campus to be more pleasing. The authors used image classification techniques to look at images of colleges and correlate certain features like colors, cars, and people to higher or lower retention rates. With three possible options of high, medium, and low retention rates, the probability that their models reached the right conclusion if they simply chose randomly was 33%. After finding that this 33%, or 0.33 mark, always fell outside of the 99% confidence intervals built around their models’ accuracies, the authors concluded that their machine learning techniques can be used to find correlations between certain environmental factors and retention rates.

Examining the role of passive design indicators in energy burden reduction: Insights from a machine learning and deep learning approach

Passive design characteristics (PDC) play a pivotal role in reducing the energy burden on households without imposing additional financial constraints on project stakeholders. However, the scarcity of PDC data has posed a challenge in previous studies when assessing their energy-saving impact. To tackle this issue, this research introduces an innovative approach that combines deep learning-powered computer vision with machine learning techniques to examine the relationship between PDC and energy burden in residential buildings. In this study, we employ a convolutional neural network computer vision model to identify and measure key indicators, including window-to-wall ratio (WWR), external shading, and operable window types, using Google Street View images within the Chicago metropolitan area as our case study. Subsequently, we utilize the derived passive design features in conjunction with demographic characteristics to train and compare various machine learning methods. These methods encompass Decision Tree Regression, Random Forest Regression, and Support Vector Regression, culminating in the development of a comprehensive model for energy burden prediction. Our framework achieves a 74.2 % accuracy in forecasting the average energy burden. These results yield invaluable insights for policymakers and urban planners, paving the way toward the realization of smart and sustainable cities.

Using google street view to reveal environmental justice: Assessing public perceived walkability in macroscale city

Walkability is an important issue in urban planning equity, which is primarily influenced by the objective environment and subjective perception. However, assessing the objective environment of the city on a large scale or obtaining the general public’s perceptual evaluation of the urban environment with less cost is challenging. This research adopted two-stage studies to identify the relationship between streetscape elements and perceived walkability using Google Street View images with machine learning in Taipei City. In Study I, the Zhongzheng District in Taipei was selected as the sample area and successfully developed a walkability prediction model that integrates street elements segmented by semantic segmentation technique, with the perceived walkability. Roads and terrains were identified as key predictors that affect perceived walkability in this random forest regression model. Based on this prediction model, we expanded the walkability assessment citywide with semantic segmentation in Study II, and the citywide walkability maps were produced. Accordingly, the socio-spatial equality of walkability was further audited. We further adopted spatial linear regression analysis to examine the relationship between neighborhood socioeconomic indicators (individual income, elderly %, and less educated %) and perceived walkability. Through the geographically weighted regression analysis, the results indicated that situated in peripheral areas were more sensitive to local socioeconomic indicators. This highlights the significant presence of social-spatial vulnerability in the walkability of Taipei City. Our research demonstrated the feasibility of using machine learning to audit urban socio-spatial justice from urban micro-to-macro scales.

Data-driven framework for analyze the visual quality of streets: A systematic measurement of street quality through street view images utilizing deep learning algorithms

The study developed a framework that utilizes deep learning algorithms to analyse the visual quality of streets (VQOS) through street view images (SVIs), while overcoming the constraints observed in existing practices. The study consisted of four main stages, including literature reviews and expert discussions, development of deep learning algorithms, testing of developed algorithms, and validation. The study developed both convolutional neural network (CNN) and feed forward neural network (FFNN) algorithms, using 2684 street view images extracted from Google Street View images and Cityscape datasets, rated by an expert panel and the general public. The proposed framework comprises stages such as the collection and rating of street view images, image processing, developing and training the CNN, and testing and validating stages. The proposed framework achieved 90.51% internal validation accuracy using the accuracy metric in Keras and 86.7% external validation accuracy, with an accepted level of kappa accuracy of 80%. Urban planners, designers, architects, and landscape architects can use this framework as a tool for quantitatively measuring and mapping the visual quality of streets and assessing the impact of proposed developments and guidelines on the visual quality of streets. This proposed framework will enhance the effectiveness of their new proposals and designs.