Google Street View Images Research Articles

A street-view-based method to detect urban growth and decline: A case study of Midtown in Detroit, Michigan, USA

Urban growth and decline occur every year and show changes in urban areas. Although various approaches to detect urban changes have been developed, they mainly use large-scale satellite imagery and socioeconomic factors in urban areas, which provides an overview of urban changes. However, since people explore places and notice changes daily at the street level, it would be useful to develop a method to identify urban changes at the street level and demonstrate whether urban growth or decline occurs there. Thus, this study seeks to use street-level panoramic images from Google Street View to identify urban changes and to develop a new way to evaluate the growth and decline of an urban area. After collecting Google Street View images year by year, we trained and developed a deep-learning model of an object detection process using the open-source software TensorFlow. By scoring objects and changes detected on a street from year to year, a map of urban growth and decline was generated for Midtown in Detroit, Michigan, USA. By comparing socioeconomic changes and the situations of objects and changes in Midtown, the proposed method is shown to be helpful for analyzing urban growth and decline by using year-by-year street view images.

Google Street View Images as Predictors of Patient Health Outcomes, 2017-2019.

Collecting neighborhood data can both be time- and resource-intensive, especially across broad geographies. In this study, we leveraged 1.4 million publicly available Google Street View (GSV) images from Utah to construct indicators of the neighborhood built environment and evaluate their associations with 2017–2019 health outcomes of approximately one-third of the population living in Utah. The use of electronic medical records allows for the assessment of associations between neighborhood characteristics and individual-level health outcomes while controlling for predisposing factors, which distinguishes this study from previous GSV studies that were ecological in nature. Among 938,085 adult patients, we found that individuals living in communities in the highest tertiles of green streets and non-single-family homes have 10–27% lower diabetes, uncontrolled diabetes, hypertension, and obesity, but higher substance use disorders—controlling for age, White race, Hispanic ethnicity, religion, marital status, health insurance, and area deprivation index. Conversely, the presence of visible utility wires overhead was associated with 5–10% more diabetes, uncontrolled diabetes, hypertension, obesity, and substance use disorders. Our study found that non-single-family and green streets were related to a lower prevalence of chronic conditions, while visible utility wires and single-lane roads were connected with a higher burden of chronic conditions. These contextual characteristics can better help healthcare organizations understand the drivers of their patients’ health by further considering patients’ residential environments, which present both risks and resources.

Estimation of Mean Radiant Temperature in Urban Canyons Using Google Street View: A Case Study on Seoul

Extreme heat exposure has severe negative impacts on humans, and the issue is exacerbated by climate change. Estimating spatial heat stress such as mean radiant temperature (MRT) is currently difficult to apply at city scale. This study constructed a method for estimating the MRT of street canyons using Google Street View (GSV) images and investigated its large-scale spatial patterns at street level. We used image segmentation using deep learning to calculate the view factor (VF) and project panorama into fisheye images. We calculated sun paths to estimate MRT using panorama images from Google Street View. This paper shows that regression analysis can be used to validate between estimated short-wave, long-wave radiation and the measurement data at seven field measurements in the clear-sky (0.97 and 0.77, respectively). Additionally, we compared the calculated MRT and land surface temperature (LST) from Landsat 8 on a city scale. As a result of investigating spatial patterns of MRT in Seoul, South Korea, we found that a high MRT of street canyons (>59.4 °C) is mainly distributed in open space areas and compact low-rise density buildings where the sky view factor is 0.6–1.0 and the building view factor (BVF) is 0.35–0.5, or west-east oriented street canyons with an SVF of 0.3–0.55. However, high-density buildings (BVF: 0.4–0.6) or high-density tree areas (Tree View Factor, TVF: 0.6–0.99) showed low MRT (<47.6). The mapped MRT results had a similar spatial distribution to the LST; however, the MRT was lower than the LST in low tree density or low-rise high-density building areas. The method proposed in this study is suitable for a complex urban environment consisting of buildings, trees, and streets. This will help decision makers understand spatial patterns of heat stress at the street level.

Assessing soil sealing and ecosystem services of urban front yards using Google Street View: A case study in Bloemhof district Rotterdam, the Netherlands

Private gardens play an important role as urban green space in cities and can improve the microclimate and address the impacts of climate change. Paving over front yards, soil sealing, reduces the environmental benefit of front yards. Residential private front yards comprise a considerable portion of land and green space in the suburbs of cities. Currently there is no method available to determine sealed soil percentages of private front yards. This study took place in the Bloemhof suburban district in Rotterdam. Four streets were selected and a total of 123 houses with 105 private front yards were assessed. Five sealed soil reference categories were defined and Google Street View (GSV) images were used to assess the front yards. This study found that the aggregated sealed soil percentages of the private front yards in the four selected streets are very high: 69%, 78%, 96%, and 97%. These front yards have a significant greening potential. The new insight in this study is that the use of Google Street View images for categorisation of front yards leads to values for individual front yard that can be used for comparison and for establishing sealed soil values per street.

Heat Vulnerability and Street-Level Outdoor Thermal Comfort in the City of Houston: Application of Google Street View Image Derived SVFs

Heat Vulnerability and Street-Level Outdoor Thermal Comfort in the City of Houston: Application of Google Street View Image Derived SVFs

Applying machine learning and google street view to explore effects of drivers’ visual environment on traffic safety

This study aims to explore the effects of drivers’ visual environment on speeding crashes by using different machine learning techniques. To obtain the data of drivers’ visual environment in the real world, a framework was proposed to obtain the Google street view (GSV) images. Deep neural network and computer vision technologies were applied to obtain the clustering and depth information from the GSV images. To reflect drivers’ visual environment in the real world, the coordinate transformation was conducted, and several visual measures were proposed and calculated. Three different tree-based ensemble models (i.e., random forest, adaptive boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost)) were applied to estimate the number of speeding crashes and the comparison results showed that XGBoost could provide the best data fit. The explainable machine learning method were applied to explore the effects of drivers’ visual environment and other features on speeding crashes. The results validated the visual environment data obtained by the proposed method for the speeding crash analysis. It was suggested that the proportion of trees in the drivers’ view and the proportion of road length with trees could reduce speeding crashes. In addition, the complexity level of drivers’ visual environment was found to increase the crash occurrence. This study provided new insights to obtain the detailed information from GSV images for traffic safety analysis. The findings based on the explainable machine learning could also provide road planners and engineers clear suggestions to select appropriate countermeasures to enhance traffic safety.

Exploring associations between streetscape factors and crime behaviors using Google Street View images

Exploring associations between streetscape factors and crime behaviors using Google Street View images

A simplified evaluation method of rooftop solar energy potential based on image semantic segmentation of urban streetscapes

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

Google street view image availability in the Bronx and San Diego, 2007-2020: Understanding potential biases in virtual audits of urban built environments.

Google Street View's 'Time Machine' feature holds promise for longitudinal street audits of built and natural environments for urban health research. As images are only available when Google collected data, differential image availability over time and place could bias audit data quality. We assessed image availability at 2000 randomly selected locations within the Bronx and San Diego from which Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants were recruited. In the Bronx, a mean of 7.4 images (95% CI: 7.2,7.5) were available at each location, and 63% of those locations had imagery in 2007 and 2019. In San Diego, fewer images were available (mean 5.4, 95% CI: 5.2,5.6) especially on minor streets (mean 4.4, 95% CI: 4.1,4.6). Image availability was more spatially clustered in San Diego (Moran's I 0.14) than the Bronx (Moran's I 0.04). Differential image availability may affect precision of neighborhood change estimates assessed by longitudinal virtual audit.

Cognitive Based Authentication Protocol for Distributed Data and Web Technologies.

The objective of the verification process, besides guaranteeing security, is also to be effective and robust. This means that the login should take as little time as possible, and each time allow for a successful authentication of the authorised account. In recent years, however, online users have been experiencing more and more issues with recalling their own passwords on the spot. According to research done in 2017 by LastPass on its employees, the number of personal accounts assigned to one business user currently exceeds 191 profiles and keeps growing. Remembering these many passwords, especially to applications which are not used every week, seems to be impossible without storing them either on paper, in a password manager, or saved in a file somewhere on a PC. In this article a new verification model using a Google Street View image as well as the user’s personal experience and knowledge will be presented. The purpose of this scheme is to assure secure verification by creating longer passwords as well as delivering a ‘password reminder’ already embedded into the login scheme.

Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimation

ABSTRACT Street view imagery such as Google Street View is widely used in people’s daily lives. Many studies have been conducted to detect and map objects such as traffic signs and sidewalks for urban built-up environment analysis. While mapping objects in the horizontal dimension is common in those studies, automatic vertical measuring in large areas is underexploited. Vertical information from street view imagery can benefit a variety of studies. One notable application is estimating the lowest floor elevation, which is critical for building flood vulnerability assessment and insurance premium calculation. In this article, we explored the vertical measurement in street view imagery using the principle of tacheometric surveying. In the case study of lowest floor elevation estimation using Google Street View images, we trained a neural network (YOLO-v5) for door detection and used the fixed height of doors to measure doors’ elevation. The results suggest that the average error of estimated elevation is 0.218 m. The depthmaps of Google Street View were utilized to traverse the elevation from the roadway surface to target objects. The proposed pipeline provides a novel approach for automatic elevation estimation from street view imagery and is expected to benefit future terrain-related studies for large areas.

Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes.

Characteristics of the neighborhood built environment influence health and health behavior. Google Street View (GSV) images may facilitate measures of the neighborhood environment that are meaningful, practical, and adaptable to any geographic boundary. We used GSV images and computer vision to characterize neighborhood environments (green streets, visible utility wires, and dilapidated buildings) and examined cross-sectional associations with chronic health outcomes among patients from the University of California, San Francisco Health system with outpatient visits from 2015 to 2017. Logistic regression models were adjusted for patient age, sex, marital status, race/ethnicity, insurance status, English as preferred language, assignment of a primary care provider, and neighborhood socioeconomic status of the census tract in which the patient resided. Among 214,163 patients residing in California, those living in communities in the highest tertile of green streets had 16–29% lower prevalence of coronary artery disease, hypertension, and diabetes compared to those living in communities in the lowest tertile. Conversely, a higher presence of visible utility wires overhead was associated with 10–26% more coronary artery disease and hypertension, and a higher presence of dilapidated buildings was associated with 12–20% greater prevalence of coronary artery disease, hypertension, and diabetes. GSV images and computer vision models can be used to understand contextual factors influencing patient health outcomes and inform structural and place-based interventions to promote population health.

Web Scraper Utilizes Google Street View Images to Power a University Tour

Due to the outbreak of the Covid-19 pandemic, college tours are no longer available, so many students have lost the opportunity to see their dream school’s campus. To solve this problem, we developed a product called “Virtourgo,” a university virtual tour website that uses Google Street View images gathered from a web scraper allowing students to see what college campuses are like even when tours are unavailable during the pandemic. The project consists of 3/4 parts: the web scraper script, the GitHub server, the Google Domains DNS Server, and the HTML files. Some challenges we met include scraping repeated pictures and letting the HTML dropdown menu jump to the correct location. We solved these by implementing Python and Javascript functions that specifically target such challenges. Finally, after experimenting with all the functions of the web scraper and website, we confirmed that it works as expected and can scrape and deliver tours of any university campus or public buildings we want.

Visual and aesthetic markers of gentrification: agency of mapping and tourist destinations

Framed within the context of contemporary place making and the urban tourism destination, a novel trans-disciplinary approach to mapping and interpreting gentrification and cityscape change is presented. A combination of multi-scalar cartographic and visual techniques, including Google Street View (GSV) and geo-tagged images (Photo Sphere), is applied within the setting of Valletta, Malta, to convey cartographically the dynamism of urban transformations and detect tourism-led gentrification in a rapid way through the identification of the visual and aesthetic markers of gentrification. Utilising a critical approach, such map-making can inform and transform the understanding of the processes underpinning urban change and their cartographic and visual representation. It identifies and highlights issues of image and street-level ‘realities’ for both (re)imagining and marketing place. Individual tourists are able to access and utilise GSV and other mobile digital imaging and cartographic tools, as well as social media, to create their own, potentially counter, place-imaginaries and interpretations of urban change, thereby encouraging and/or discouraging visiting the city.

A National Land Use Regression Model for NO2 using Street View Imagery and Satellite-based Air Quality Estimates

BACKGROUND AND AIM: Characterizing air pollution with high spatiotemporal resolution is important for assessing health impacts and crafting control strategies. Land Use Regression (LUR) is often used to estimate fine-scale ambient concentrations; national-scale LURs typically employ hundreds of geographic features which offer information at neighborhood or regional scales. In this study, we develop national NO₂ models with (1) built environment features derived from street view imagery and (2) satellite estimates of air quality. Both data sources are publicly available and consistent across large geographies. METHODS: We collected NO₂ concentrations at EPA monitors and satellite-based NO₂ tropospheric column abundance during 2007-2015. In a previous study, we successfully developed single city LUR models for measures of particulates using variables extracted from Google Street View (GSV) images. Following the same method, we extracted features from GSV images (n=242836) within 500m of NO₂ monitors using a deep learning model (PSPNet). We used only GSV images collected during the same year of the NO₂ observations. For comparison, we developed two random forest models: 1) a full model with both GSV features and satellite-based estimates of NO₂ and 2) a model with only GSV features. RESULTS:Our full model (GSV + satellite) had good performance (10-fold cross validation [CV] R²: 0.69; mean absolute error [MAE]: 2.04 ppb). When only GSV features within 500m of NO₂ monitors were included in the model, performance decreased (10-fold CV R²: 0.50; MAE: 2.85 ppb), consistent with previous national LUR studies. We plan to improve these models by exploring the impact of GSV image availability and density for monitor-year pairs, expanding the buffer area for collecting images, and adding kriging into our models. CONCLUSIONS:Our findings suggest that street view imagery and satellite estimates of air quality have great potential for building large scale air quality models (national or global) under a unified framework. KEYWORDS: air pollution, modeling, oxides of nitrogen

Street-View Greenspace Exposure and Objective Daily Rest-Activity Patterns

BACKGROUND AND AIM: Rest-activity patterns (sleep and physical activity) are modifiable risk factors for obesity. The sparse research on greenspace and sleep or physical activity in children is dominated by satellite-derived measures of greenspace, which do not capture ground-level exposures. Google Street View (GSV) images measure greenspace exposures as participants experience them and may be most relevant for health. We aimed to examine GSV-based greenspace and objective rest-activity patterns in adolescents in Project Viva, a cohort in eastern Massachusetts participating in the Environmental influences on Child Health Outcomes (ECHO) consortium. METHODS: We applied deep learning algorithms to GSV images from 2012-2016 to derive metrics of greenspace (e.g., % trees, %grass within each image) within 250m of participant’s residential addresses. We derived rest-activity metrics from the early adolescence visit (2012-2016; median age 12.7), when participants completed 5 days of wrist actigraphy for 10 hours/day. We used linear regression to examine associations between GSV-based greenspace and rest-activity patterns, adjusting for child’s sex, race/ethnicity, and age; mother’s education and marital status; father’s education; household income; neighborhood median income and population density. RESULTS:In unadjusted cross-sectional analyses (N=505), higher %grass, %trees, %plants, %fields, and %flowers combined (%total greenspace) was associated with lower activity in the least active 5-hour period (L5) in the 24-hour cycle, suggesting more consolidated sleep (L5 difference per IQR [24%] increase in %greenspace: -10.3 [95%CI -16.4,-4.2]), and slightly higher relative amplitude (RA), reflecting both higher activity during wakefulness and more restful sleep at night (RA difference per IQR increase in %greenspace: 0.01 (95%CI 0.01, 0.02)). These associations were consistent for both %grass and %trees. However, in multivariable models, these associations were no longer present. No associations were observed between GSV-based %greenspace exposure and moderate-vigorous nor light physical activity. CONCLUSIONS:GSV-based greenspace exposure was not associated with objective rest-activity patterns among participants in early adolescence. KEYWORDS: Greenspace, Google Street View, Deep Learning, Accelerometry, Rest Activity, Children

Street-View Greenspace Exposure and Objective Sleep Characteristics

BACKGROUND AND AIM: Epidemiologic studies on the association of residential greenness with children’s sleep quality are limited. Furthermore, most studies rely on satellite-derived measures of greenspace, which do not capture ground-level visible exposures. Google Street View (GSV) images offer the opportunity to measure greenspace from a street-based view and capture greenspace that may be most relevant for health. We aim to examine associations of GSV-based greenspace with objective sleep characteristics in Project Viva, a highly-phenotyped cohort in eastern Massachusetts participating in the Environmental influences on Child Health Outcomes (ECHO) consortium. METHODS: We applied the PSPNET deep learning segmentation algorithm to GSV images from 2007-2010 (mid-childhood visit) and 2012-2016 (early adolescence visit) to derive novel metrics of greenspace (e.g. % trees, %grass within each 360 degree image) at a 250m radius around participant’s residential addresses (N=717). In early adolescence (mean[SD] age 13.0[0.7] years), participants completed 5 days of wrist actigraphy recording for 10 hours/day. Sleep duration and efficiency were derived from actigraph data. We used linear regression to examine associations of GSV-based greenness metrics at mid-childhood and early adolescence with sleep at early adolescence, adjusting for child’s sex, race/ethnicity, and age, mother’s education and marital status, father’s education, household income, neighborhood median income, and population density. RESULTS:Among the 500 adolescents, 249 were girls. In unadjusted models, mid-childhood GSV-based total greenness (per interquartile range) was associated with longer sleep duration at early adolescence (9.3 min/day; 95%CI:3.2,15.5). This association was mostly driven by trees (8.2; 95%CI:1.7,14.6). In early adolescence, unadjusted associations were similar. However, in all fully adjusted models, the association was no longer present (e.g. 1.1; 95%CI:-5.5,7.7 for mid-childhood greenness). No associations were observed between GSV-based greenness and sleep efficiency. CONCLUSIONS:GSV-based greenness was not associated with sleep duration and efficiency in early adolescence among Project Viva participants. KEYWORDS: Built environment, green space, Children's environmental health, Environmental epidemiology

Predicting Within-City Variations in Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Columbia Using Open Source Data and Images

BACKGROUND AND AIM: Outdoor ultrafine particles (UFP; 100 nm) and black carbon (BC) vary greatly within cities and may have adverse impacts on human health such as cardiovascular mortality and brain tumour incidence. Traditional regression methods for estimating spatial distributions of ambient air pollution depend on extensive, curated geospatial information system databases. This has resulted in a disparity wherein little is known about local air pollution in the data-sparse settings of low- and middle- income countries. We used a hybrid approach to develop new models to estimate within-city spatial variations in outdoor UFP and BC concentrations across Bucaramanga, Colombia. METHODS: We conducted a large-scale mobile monitoring campaign over twenty days in 2019. Land use regression (LUR) models were developed using land use parameters from curated and open-source databases. Convolutional Neural Network (CNN) models were developed using Google Maps satellite view and street view images. Combined models were developed by combining predictions from the LUR and CNN models. After training in the training and validation sets, predictions were generated in the test set and compared to measured values. RESULTS:The combined UFP model (R2=0.54) outperformed the CNN (R2=0.47) and land use regression (LUR) models (R2=0.47) on their own. Similarly, the combined BC model also outperformed the CNN and LUR BC models (R2 = 0.51 vs 0.43 and 0.45 respectively). Spatial variations in model performance were more stable for the CNN and combined models compared to the LUR models suggesting that the combined approach may be less likely to contribute to differential exposure measurement error in epidemiological studies. CONCLUSIONS:Estimates from these models can then be applied to population-based cohorts in order to evaluate population health risks. Additionally, our findings demonstrated that satellite and street-level images can be combined with a traditional LUR modelling approach to improve predictions of within-city spatial variations in outdoor UFP and BC concentrations. KEYWORDS: Ultrafine particles, Black Carbon, Deep learning, Images, Land use regression

Measuring vegetation greenery in park using iPhone panoramic image and a new green vegetation extraction index

Park greenery contributes substantial health benefits for local residents. Few data source however is available to quantitatively measure the vegetation greenery along park trails. In this study, a typical park region in Homestead, Florida was selected as the study area. The iPhone panoramic images in 400 randomly generated sampling sites were examined to depict the vegetation greenery along park trails. A green vegetation extraction index (GVEI) was proposed to distinguish the green vegetation in the images, and the green vegetation index was computed to grade the sampling sites based on the GVEI results. The results demonstrated that the overall (kappa coefficient) accuracies of green vegetation classification in the sampling sites using the GVEI all exceeded 0.90 (0.87). The GVEI values obtained from iPhone and commonly used Google Street View (GSV) images showed a high correlation coefficient of 0.94, which indicated that the iPhone panoramic images were compatible with the GSV images, and suited for green vegetation identification and monitoring. The green view index of sampling sites showed discrepant patterns, 50 sites possessed the green view index of more than 30 %, indicating that people in these areas can enjoy visual and spiritual pleasure. Park managers and planners may strive to improve the vegetation greenery in the other sites to offer better health benefits for people. The method established in this study can be used as a reference for vegetation greenery monitoring in other parks.

Improving spatial data in health geographics: a practical approach for testing data to measure children\u2019s physical activity and food environments using Google Street View

BackgroundGeographic information systems (GIS) are often used to examine the association between both physical activity and nutrition environments, and children’s health. It is often assumed that geospatial datasets are accurate and complete. Furthermore, GIS datasets regularly lack metadata on the temporal specificity. Data is usually provided ‘as is’, and therefore may be unsuitable for retrospective or longitudinal studies of health outcomes. In this paper we outline a practical approach to both fill gaps in geospatial datasets, and to test their temporal validity. This approach is applied to both district council and open-source datasets in the Taranaki region of Aotearoa New Zealand.MethodsWe used the ‘streetview’ python script to download historic Google Street View (GSV) images taken between 2012 and 2016 across specific locations in the Taranaki region. Images were reviewed and relevant features were incorporated into GIS datasets.ResultsA total of 5166 coordinates with environmental features missing from council datasets were identified. The temporal validity of 402 (49%) environmental features was able to be confirmed from council dataset considered to be ‘complete’. A total of 664 (55%) food outlets were identified and temporally validated.ConclusionsOur research indicates that geospatial datasets are not always complete or temporally valid. We have outlined an approach to test the sensitivity and specificity of GIS datasets using GSV images. A substantial number of features were identified, highlighting the limitations of many GIS datasets.