Search

This study explored the association between greening and social capital in neighborhoods, considering demographics and zoning by urban planning. The target area encompassed the urban areas of Kyoto City, Japan, which has a long tradition of landscape policy and neighborhood associations. Greening was evaluated using two automated methods: 1) horizontal green coverage of the land was calculated via the Normalized Difference Vegetation Index in satellite images, and 2) green visibility in streetscape from a human perspective was estimated by combining Google Street View images and a machine learning model. Public government data were used for demographics and zoning, and social capital was evaluated using survey data from the local government. After performing the elastic net models, variables that had explanatory power for each greening index were selected. Similar reasonable associations were found for each of the indices with the zoning categories. However, for both zoning and demographics, different variables were selected. Importantly, the social capital variable was selected only for the green visibility in streetscape, showing a negative correlation between them, unlike in previous studies. These results suggest that the association between urban greening and social relationships can change depending on the context of the target regions and measurements of greening.

Abstract Introduction Wearing a helmet reduces the risk of head injuries substantially in the event of a motorcycle crash. Countries around the world are committed to promoting helmet use, but the progress has been slow and uneven. There is an urgent need for large-scale data collection for situation assessment and intervention evaluation. Methods This study proposes a scalable, low-cost algorithm to estimate helmet-wearing rates. Applying the state-of-the-art deep learning technique for object detection to images acquired from Google Street View, the algorithm has the potential to provide accurate estimates at the global level. Results Trained on a sample of 3995 images, the algorithm achieved high accuracy. The out-of-sample prediction results for all three object classes (helmets, drivers, and passengers) reveal a precision of 0.927, a recall value of 0.922, and a mean average precision at 50 (mAP50) of 0.956. Discussion The remarkable model performance suggests the algorithm’s capacity to generate accurate estimates of helmet-wearing rates from an image source with global coverage. The significant enhancement in the availability of helmet usage data resulting from this approach could bolster progress tracking and facilitate evidence-based policymaking for helmet wearing globally.

Global localization. Google Earth and the experience of place attachment in GeoGuessr, Houellebecq and Darrieussecq The purpose of this article is to analyze literary texts and other cultural practices that relate to or use content from Google Earth and, more generally, to outline what I call a contemporary glocal experience of place. The article discusses the browser game GeoGuessr and the interactive music video “The Wilderness Downtown” by Arcade Fire, along with the novel La Mer à l’envers by Marie Darrieussecq, and different works, including photographs and poems, by Michel Houellebecq. In all these instances, the transmissions, slippages, and tensions between a situated and embodied experience of place and global technological systems play a prominent role. The article shows how GeoGuessr and “The Wilderness Downtown” explore this tension from opposite points of departure: while the game fosters a sense of place within locales that are essentially unknown to the player, the music video integrates images from places that are related to the viewer’s personal memories into an otherwise impersonal animated video. In turn, Darrieussecq’s novel combines and narrativizes these opposing perspectives. Here, Calais represents a globalized space that can only be understood through glocalizing technologies, such as geoposititioning and interactive mapping. But Clève, the hometown of the protagonist, becomes unmoored through the same interaction with these technologies. Finally, the article highlights the “Houellebecq perspective” in novels, poems, and photographs. In these recurring scenes, the world is seen from above (through satellite imagery, on maps, or through airplanes windows). They revolve around the tension between the aerial view and the “horizon structure”, which permits the subject to place itself in the world and in relation to others.

Abstract Flood events can cause extensive damage to physical infrastructure, pose risks to human life, and necessitate the reoccupation and rehabilitation of affected areas. A key parameter for flood vulnerability assessment is the first floor height (FFH), which also plays an important role in setting insurance premiums. Traditional methods for FFH estimation rely on ground surveys and site inspections, yet these approaches are both time‐consuming and labor‐intensive. In this study, we propose an alternative approach based on measurements derived from Google Street View (GSV) images and Deep Learning (DL). We employ the YOLOv5s algorithm, which belongs to a family of compound‐scaled object detection models trained on the COCO dataset, for the detection of crucial building elements such as the Front Door (FD), stairs, and overall building extent. Additionally, we utilized the YOLOv5s algorithm to identify basement windows and assess the existence of basements. To validate our methodology, we conducted tests in both the Greater Toronto Area (GTA) and the state of Virginia in the United States. The results demonstrate an achievement of RMSE and Bias values of 81 cm and −50 cm for GTA, and 95 cm and −20 cm for the Virginia region, respectively.

ABSTRACTUrban tree inventories have mostly focused on the information of individual trees becausethese allows city authorities to efficiently plan urban forestation . However, single-tree urban tree inventories are expensive for municipalities, so the inventories lack detail and are often out of date. In this work, we aim to integrate the possibility of using online applications for automatic species identification with worldwide coverage Pl@ntNet and Plant.Id on Google Street View (GSV) images in order to perform cost-effective urban tree inventories at the single-tree level and evaluate the performance of the two applications through comparison with a locally trained neural network using an appropriate set of metrics. Our work showed that the Plant.Id application gave the best performance by correctly identifying plants in the city of Prato with a median accuracy of 0.73 and better performance for the most common plants: Pinus pinea 0.87, Tilia aeuropea 0.87, Platanus hybrida 0.89. The proposed method also has a limitation. Trees within parks, walking paths and private green areas cannot be photographed and identified because Google cars cannot access them. The solution to this limitation is to combine GSV images with spherical photos taken via light unmanned aircraft.

This paper aims to address the challenges associated with evaluating the impact of neighborhood environments on health outcomes. Google street view (GSV) images provide a valuable tool for assessing neighborhood environments on a large scale. By annotating the GSV images with labels indicating the presence or absence of specific neighborhood features, we can develop classifiers capable of automatically analyzing and evaluating the environment. However, the process of labeling GSV images to analyze and evaluate the environment is a time-consuming and labor-intensive task. To overcome these challenges, we propose using a multi-task classifier to enhance the training of classifiers with limited supervised GSV data. Our multi-task classifier utilizes readily available, inexpensive online images collected from Flickr as a related classification task. The hypothesis is that a classifier trained on multiple related tasks is less likely to overfit to small amounts of training data and generalizes better to unseen data. We leverage the power of multiple related tasks to improve the classifier’s overall performance and generalization capability. Here we show that, with the proposed learning paradigm, predicted labels for GSV test images are more accurate. Across different environment indicators, the accuracy, $F_{1}$ score and balanced accuracy increase up to 6% in the multi-task learning framework compared to its single-task learning counterpart. The enhanced accuracy of the predicted labels obtained through the multi-task classifier contributes to a more reliable and precise regression analysis determining the correlation between predicted built environment indicators and health outcomes. The $R^{2}$ values calculated for different health outcomes improve by up to 4% using multi-task learning detected indicators.

Pedestrian interventions for healthier and more inclusive streetscapes can be powerful mechanisms to increase the safety and comfort of walking in African cities. This article proposes a multiscale walkability analysis approach to identify both suitable streets for pedestrian travel and problematic areas requiring small-scale improvements (e.g., pavement repairs, building maintenance, streetlights, and public seating). We applied a GIS-based framework to the central urban area of Cape Town, South Africa, which presents complex social and environmental challenges. For each street-and-crossing segment, a virtual pedestrian streetscape audit tool was used to collect micro- and mesoscale environmental indicators and assess the quality of public space. This composite street-level assessment tool was weighted with a space syntax analysis indicator (i.e., spatial integration) to detect the network’s most interconnected and high-priority pathways. The Jenks natural breaks classification algorithm was used to classify scores for each segment, which ultimately found that the highest-priority streets for redevelopment are clustered in Bo-Kaap, a relatively disadvantaged, multicultural, and hilly district on Cape Town’s west side. Policy recommendations are evaluated to increase the quality of the urban environment and the city’s overall attractiveness to pedestrians. The proposed methodology facilitates more effective place management and classifies the city’s needs in improvements, minimizing both time and budget costs.

Prostorsko urejanje območij za pešce, ki zagotavlja bolj zdravo in vključujočo ulično krajino, je lahko močan mehanizem za izboljšanje varnosti in udobnosti pešačenja v afriških mestih. Avtorja v članku predlagata pristop k analizi hodljivosti na več prostorskih ravneh, s katerim se lahko določijo ulice, primerne za pešce, in problematična območja, ki zahtevajo manjše izboljšave (npr. popravilo pločnikov, boljše vzdrževanje stavb ter ureditev ulične razsvetljave in javnih klopi). Raziskovalni okvir, ki temelji na uporabi GIS, sta uporabila za središče Cape Towna v Južni Afriki, ki se spopada s kompleksnimi družbenimi in okoljskimi izzivi. Za vsak segment ulice s prehodom za pešce sta z orodjem za virtualno presojo območij za pešce zbrala podatke o okoljskih kazalnikih na mikro- in mezoravni ter proučila kakovost javnega prostora. Rezultate, dobljene z navedenim sestavljenim orodjem za presojo ulic, sta pomnožila z normalizirano vrednostjo mere prostorske sintakse (tj. integracije), na podlagi česar sta določila poti v mreži, ki so med seboj najbolj povezane in najbolj potrebne prenove. Z Jenksovo metodo naravnih mej sta razvrstila rezultate za vsak segment, na podlagi česar sta ugotovila, da so ulice, ki so najbolj potrebne prenove, zgoščene v Bo-Kaapu, razmeroma slabo razvitem, večkulturnem in hribovitem predelu v zahodnem delu Cape Towna. Na koncu sta predstavila priporočila za izboljšanje kakovosti mestnega okolja in splošne privlačnosti mesta za pešce. Predlagana metodologija omogoča učinkovitejše upravljanje krajev in razvrščanje potreb mesta po izboljšavah, s čimer se zmanjšajo stroški in poraba časa.

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day.

A widely used method for estimating fine scale long-term spatial variation in air pollution, especially for epidemiology studies, is land use regression (LUR) modeling using fixed off-road monitors. More recently, LUR models have been developed using data from mobile monitors that repeatedly measure road pollutants and mixed-effects modeling. Here, nitrogen dioxide (NO2) and black carbon (BC) predictions from two independent models were compared across streets (defined as 30–60 m road segments) (N = 30,312) and residences (N = 76,752) in Copenhagen, Denmark. The first model was Google Street View (GSV)-based mixed-effects LUR models (Google-MM) that predicted 2019 mean NO2 and BC levels, and the second was European-wide (EUW) LUR models that predicted annual mean 2010 levels at 100 m spatial resolution. Across street segments, the Spearman correlation coefficient between the 2019 NO2 from Google-MM-LUR and 2010 NO2 from EUW-LUR was 0.66, while at residences, this was 0.60. For BC, these were 0.51 across street segments and 0.40 at the residential level. The ratio of percentile 97.5 to 2.5 for NO2 across the study area streets using Google-MM NO2 was 4.5, while using EUW-LUR, this was 2.1. These NO2 ratios at residences were 3.1 using Google-MM LUR, and 1.7 using EUW-LUR. Such ratios for BC across street segments were 3.4 using Google-MM LUR and 2.3 using EUW-LUR, while at the residential level, they were 2.4 and 1.9, respectively. In conclusion, Google-MM-LUR NO2 for 2019 was moderately correlated with EUW-LUR NO2 developed in 2010 across Copenhagen street segments and residences. For BC, while Google-MM-LUR was moderately correlated with EUW-LUR across Copenhagen streets, the correlation was lower at the residential level. Overall, Google-MM-LUR revealed larger spatial contrasts than EUW-LUR.

Gathering knowledge about physical settings and visual information of places has long been of interest to a wide variety of fields as they affect the experience of observers. Previous studies have relied on on-site surveys, low-throughput methods, and limited data sources, which especially hinder analyzing waterscape features. Thus, detecting the relationships between the human perception results of large-scale urban water areas and the waterfront features at high spatial resolutions remains challenging, and worldwide studies have not been conducted. We investigate an alternative: a data-driven waterscapes evaluation approach based on computer vision (CV) to analyze water view imagery (WVI) in 16 cities around the world and measure how people perceive scenes using virtual reality (VR). We bring attention to WVI – the counterpart of street view imagery (SVI) on water bodies, which is readily available for many cities thanks to the usual SVI services, but has been entirely overlooked in research hitherto. Specifically, a deep learning model, which has been trained with 500 segmented water-level photos, was developed to analyze them, achieving the mean pixel accuracy (MPA) of 94%, which advances state of the art. These panoramic images have been assessed through a virtual experience survey in which 60 participants indicated their perceptions across multiple dimensions. Afterwards, a series of statistical analyses were conducted to determine the visual indicators that drive perceptions, and the relationship between the people’s subjective visual perceptions and objective waterscape environment as seen by machines has been established. The results take researchers and watercourse planners one step toward understanding the interactions of the perceptions and semantics of water areas globally. The large-scale dataset we produced in this research has been released openly as the first such instance of open segmented water view imagery, and it is intended to support future studies.

Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO2), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 – March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables.We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO2.This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).

periphery looks at you with hate. This phrase in red neon struck the visitors of Landscapes, an exhibition by Domenico Antonio Mancini in the Lia Rumma Gallery in Naples, in 2019. It was not addressed to the public but to the nineteenth-century pictorial views relocated in the last room of the exhibition, as if repainted by the immaterial vandalism of the colored light. The exhibition’s theme was the visibility of contemporary suburban environments, now accessible through Google street view visualizations. Mancini’s non-representational Landscapes are Google addresses, not aesthetically appreciable but pragmatically challenging: the visitor must type on a touch screen to access to the outskirts of Naples, Palermo, Catania, Rome and Milan. The words at the end of the exhibition almost literally replicated a black graffiti drawn on Palazzo Missori in Milan. Are these relocation and remediation just about artification? What happens when, browsing Google street view, I take a close look at that graffiti recalling the political protests during the streets the 1960s? The deictic transitability in space and time of the hypermediated image can activate a fortuitous narrative and an open interpretation by looking back on the present as if it were past. Even a digital landscape is a moral landscape like that of public art for Danto, with a «internal beauty» whose meaning is not already embodied in architectural and artistic media but accessible and shareable through our portable devices.

3D building models are an established instance of geospatial information in the built environment, but their acquisition remains complex and topical. Approaches to reconstruct 3D building models often require existing building information (e.g.their footprints) and data such as point clouds, which are scarce and laborious to acquire, limiting their expansion. In parallel, street view imagery (SVI) has been gaining currency, driven by the rapid expansion in coverage and advances in computer vision (CV), but it has not been used much for generating 3D city models. Traditional approaches that can use SVI for reconstruction require multiple images, while in practice, often only few street-level images provide an unobstructed view of a building. We develop the reconstruction of 3D building models from a single street view image using image-to-mesh reconstruction techniques modified from the CV domain. We regard three scenarios: (1) standalone single-view reconstruction; (2) reconstruction aided by a top view delineating the footprint; and (3) refinement of existing 3D models, i.e.we examine the use of SVI to enhance the level of detail of block (LoD1) models, which are common. The results suggest that trained models supporting (2) and (3) are able to reconstruct the overall geometry of a building, while the first scenario may derive the approximate mass of the building, useful to infer the urban form of cities. We evaluate the results by demonstrating their usefulness for volume estimation, with mean errors of less than 10% for the last two scenarios. As SVI is now available in most countries worldwide, including many regions that do not have existing footprint and/or 3D building data, our method can derive rapidly and cost-effectively the 3D urban form from SVI without requiring any existing building information. Obtaining 3D building models in regions that hitherto did not have any, may enable a number of 3D geospatial analyses locally for the first time.

With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment.

An urban built environment is an important part of the daily lives of urban residents. Correspondingly, a poor design can lead to psychological stress, which can be harmful to their psychological and physical well-being. The relationship between the urban built environment and the perceived psychological stress of residents is a significant in many disciplines. Further research is needed to determine the stress level experienced by residents in the built environment on a large scale and identify the relationship between the visual components of the built environment and perceived psychological stress. Recent developments in big data and deep learning technology mean that the technical support required to measure the perceived psychological stress of residents has now become available. In this context, this study explored a method for a rapid and large-scale determination of the perceived psychological stress among urban residents through a deep learning approach. An empirical study was conducted in Gangnam District, Seoul, South Korea, and the SegNet deep learning algorithm was used to segment and classify the visual elements of street views. In addition, a human–machine adversarial model using random forest as a framework was employed to score the perception of the perceived psychological stress in the built environment. Consequently, we found a strong spatial autocorrelation in the perceived psychological stress in space, with more low-low clusters in the urban traffic arteries and riverine areas in Gangnam district and more high-high clusters in the commercial and residential areas. We also analyzed the street view images for three types of stress perception (i.e., low, medium and high) and obtained the percentage of each street view element combination under different stresses. Using multiple linear regression, we found that walls and buildings cause psychological stress, whereas sky, trees and roads relieve it. Our analytical study integrates street view big data with deep learning and proposes an innovative method for measuring the perceived psychological stress of residents in the built environment. The research methodology and results can be a reference for urban planning and design from a human centered perspective.

Abstract Background Although previous research has highlighted the association between the built environment and individual health, methodological challenges in assessing the built environment remain. In particular, many researchers have demonstrated the high inter-rater reliability of assessing large or objective built environment features and the low inter-rater reliability of assessing small or subjective built environment features using Google Street View. New methods for auditing the built environment must be evaluated to understand if there are alternative tools through which researchers can assess all types of built environment features with high agreement. This paper investigates measures of inter-rater reliability of GigaPan®, a tool that assists with capturing high-definition panoramic images, relative to Google Street View. Methods Street segments (n = 614) in Pittsburgh, Pennsylvania in the United States were randomly selected to audit using GigaPan® and Google Street View. Each audit assessed features related to land use, traffic and safety, and public amenities. Inter-rater reliability statistics, including percent agreement, Cohen’s kappa, and the prevalence-adjusted bias-adjusted kappa (PABAK) were calculated for 106 street segments that were coded by two, different, human auditors. Results Most large-scale, objective features (e.g. bus stop presence or stop sign presence) demonstrated at least substantial inter-rater reliability for both methods, but significant differences emerged across finely detailed features (e.g. trash) and features at segment endpoints (e.g. sidewalk continuity). After adjusting for the effects of bias and prevalence, the inter-rater reliability estimates were consistently higher for almost all built environment features across GigaPan® and Google Street View. Conclusion GigaPan® is a reliable, alternative audit tool to Google Street View for studying the built environment. GigaPan® may be particularly well-suited for built environment projects with study settings in areas where Google Street View imagery is nonexistent or updated infrequently. The potential for enhanced, detailed imagery using GigaPan® will be most beneficial in studies in which current, time sensitive data are needed or microscale built environment features would be challenging to see in Google Street View. Furthermore, to better understand the effects of prevalence and bias in future reliability studies, researchers should consider using PABAK to supplement or expand upon Cohen’s kappa findings.

Abstract Background The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. Methods We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level. Results Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes. Conclusions Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

Human well-being is affected by the design quality of the city in which they live and walk. This depends primarily on specific physical characteristics and how they are aggregated together. Many studies have highlighted the great potential of photographic data shared on the Flickr platform for analyzing environmental perceptions in landscape and urban planning. Other researchers have used panoramic images from the Google Street View (GSV) web service to extract data on urban quality. However, at the urban level, there are no studies correlating quality perceptions detected by social media platforms with spatial geographic characteristics through geostatistical models. This work proposes the analysis of urban quality in different areas of the Livorno city through a methodological approach based on Geographical Random Forest regression. The result offers important insights into the physical characteristics of a street environment that contribute to the more abstract qualities of urban design.

Socioeconomic indicators are essential to help design and monitor the impact of public policies on society. Such indicators are usually obtained through census data collected at 10-year intervals, which are not only temporally coarse but expensive. Over recent years other ways of collecting data and producing these indicators have been explored, in particular using the new surveillance capabilities that remote observations can provide. The objective of this paper is to evaluate the assessment of socioeconomic indicators using street-view imagery, through a case study conducted in a region of Brazil, the Vale do Ribeira, one of the poorest semi-rural regions in Brazil. In this study we used socioeconomic indicators collected by the Brazilian Institute of Geography and Statistics (IBGE) and used Google Street View (GSV) images as our source of remote observations. A pre-trained convolutional neural network (CNN) was used to predict socio-economic indicators from GSV. To evaluate the performance of the classifier, we performed five-fold cross-validation between the predicted indicator and its true value. The best performance was obtained for the highest income class, with 80% of correct prediction. We conclude that the method has the potential to predict socioeconomic indicators across a large area with social challenges such as Vale do Ribeira, and that the network model is general enough to be used even when the imagery dataset is from semi-rural areas. This demonstrates the applicability of GSV datasets for similar settings and perhaps ensuring their replicability, which is a scientific requirement that requires further experimentation/evaluation.

This study takes one step further to complement the application of a method for mapping informal green spaces (IGSs) using an efficient combination of open-source data with simple tools and algorithms. IGSs are unofficially recognized by the government as vegetation spaces designed for recreation, gardening, and forestry in urban areas. Due to the economic crisis, many formal green spaces such as urban parks and garden projects have been postponed, while IGSs have significant potential as green space retrofits. However, because they are small and spatially continuous and cannot be fully detected via airborne surveys, they are surveyed in small areas and neglected by government and city planners. Therefore, in this research, we combined the use of Google Street View (GSV) data with machine learning to develop a survey method that can be used to survey a wide area at once. Deeplab V3+ was used to segment the semantics based on the model created using 1000 labelled photos, with an accuracy rate of nearly 65%. Applying this method gave high accuracy in Ichikawa, Japan, with 3029 photos, and matched the results of a field survey in a previous study. In contrast, low accuracy was seen in Ho Chi Minh City, with 204 photos, where the quality of the GSV data was considerably lower.

This study evaluates the use of virtual, human-interpreted, field observations using Google Street View (GSV) to examine the presence of conditions that may be used to analyze green gentrification in the Global South. We propose that green gentrification is characterized by the introduction or improvement of green amenities (such as parks) as well as corresponding changes to the urban context (such as facade materials). While virtual field observations have been used to examine neighborhood context for other applications, this method has not yet been applied to the study of green gentrification, nor in the Global South. Using one urban park located in Talca, Chile, and in three urban parks located in Santiago, Chile, we sought to address the following research questions: (1) How do in situ and virtual field observations compare as methods of evaluating green amenities and urban context? (2) What characteristics of green amenities and urban context must be addressed to investigate green gentrification in the Global South? (3) How do indicators of green amenities and urban context observed via virtual field observations indicate the potential for green gentrification? In order to observe the streetside conditions of the neighborhoods surrounding established, improved, and new parks, we utilize the ground-level 360° imagery through GSV as an alternative to in situ studies, which can be time-consuming, expensive, and logistically challenging. Features related to the land use composition, building materials used, and the presence of aesthetic improvements and pedestrian amenities were noted as potential indicators of gentrification, and the correspondence between the two methods of observation were evaluated. Results indicate that virtual field observations can provide a promising method that may facilitate the identification and investigation of the effects of green gentrification in the Global South, broadening the scope and application of this research. This comparison offers insight into the use and comparison of virtual and in situ observations for identifying green gentrification in the Global South and for the applicability of the virtual observation method in this heterogeneous urban landscape, especially in cases with unreliable or unavailable data.

Abstract Background Altering micro-scale features of neighborhood walkability (e.g., benches, sidewalks, and cues of social disorganization or crime) could be a relatively cost-effective method of creating environments that are conducive to active living. Traditionally, measuring the micro-scale environment has required researchers to perform observational audits. Technological advances have led to the development of virtual audits as alternatives to observational field audits with the enviable properties of cost-efficiency from elimination of travel time and increased safety for auditors. This study examined the reliability of the Virtual Systematic Tool for Evaluating Pedestrian Streetscapes (Virtual-STEPS), a Google Street View-based auditing tool specifically designed to remotely assess micro-scale characteristics of the built environment. Methods We created Virtual-STEPS, a tool with 40 items categorized into 6 domains (pedestrian infrastructure, traffic calming and streets, building characteristics, bicycling infrastructure, transit, and aesthetics). Items were selected based on their past abilities to predict active living and on their feasibility for a virtual auditing tool. Two raters performed virtual and field audits of street segments in Montreal neighborhoods stratified by the Walkscore that was used to determine the ‘walking-friendliness’ of a neighborhood. The reliability between virtual and field audits (n = 40), as well as inter-rater reliability (n = 60) were assessed using percent agreement, Cohen’s Kappa statistic, and the Intra-class Correlation Coefficient. Results Virtual audits and field audits (excluding travel time) took similar amounts of time to perform (9.8 versus 8.2 min). Percentage agreement between virtual and field audits, and for inter-rater agreement was 80% or more for the majority of items included in the Virtual-STEPS tool. There was high reliability between virtual and field audits with Kappa and ICC statistics indicating that 20 out of 40 (50.0%) items had almost perfect agreement and 13 (32.5%) items had substantial agreement. Inter-rater reliability was also high with 17 items (42.5%) with almost perfect agreement and 11 (27.5%) items with substantial agreement. Conclusions Virtual-STEPS is a reliable tool. Tools that measure the micro-scale environment are important because changing this environment could be a relatively cost-effective method of creating environments that are conducive to active living.

Esta investigación estudia el paisaje lingüístico del barrio de Ruzafa en Valencia (España), conocido por su carácter multicultural y multilingüístico debido al establecimiento de población migrante en las últimas décadas del siglo XX y a su transformación en un espacio turístico como consecuencia del proceso de gentrificación en los primeros años del siglo XXI. El objetivo es explorar a través de los signos del paisaje lingüístico los cambios socio-demográficos desde un enfoque diacrónico y contrastivo. La metodología está basada en la utilización combinada de la herramienta Google Street View y con la toma de signos en los mismos espacios del barrio, con lo que se ha configurado un corpus fotográfico de 212 signos, imágenes de rótulos de comercios privados del barrio, de los años 2008 y 2018. El análisis de las lenguas que aparecen en los signos y cómo se relacionan entre sí nos ha permitido conocer el grado y el tipo de multilingüismo que han aportado al barrio en estos dos puntos de su historia los diferentes grupos de población, especialmente los de origen migrante, y las consecuencias lingüísticas de la gentrificación sobre su paisaje lingüístico.

A composição do ambiente pode exercer impactos sobre comportamentos, no entanto, esta relação permanece incerta até que qualidades e a paisagem das cidades possam ser analisadas empiricamente. Imagens obtidas através do Google Street View (GSV) possibilitam um grande volume de dados para avaliação automatizada das características ambientais. Técnicas de deep learning tem avançado na identificação de elementos compositivos do ambiente construído. Neste sentido, este estudo busca investigar e testar um procedimento de identificação da configuração e composição da paisagem urbana, por meio da classificação de imagens obtidas pelo GSV. A partir de um banco de imagens de três bairros de Londrina-PR, um modelo de deep learning para classificação de imagens foi proposto. O modelo obteve um bom desempenho, atribuindo corretamente 87,6% das amostras dos respectivos bairros do estudo de caso. Características compositivas foram empiricamente identificadas, considerando a distribuição das amostras no espaço de busca obtido. O modelo proposto contribui na definição de recortes espaciais bem como na mensuração de qualidades ambientais, otimizando coletas de dados, ampliando amostras e conferindo objetividade aos resultados. Esta abordagem contribui na expansão das escalas analíticas da cidade, identificando padrões compositivos e relacionais para o entendimento de elementos influentes no comportamento humano.

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 (

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.

Google Street View (GSV) was used for urban land use classification, together with airborne light detection and ranging (LiDAR) data and high resolution orthoimagery, by a parcel-based method. In this data article, we present the input raw GSV images, intermediate products of GSV images, and final urban land use classification data that are related to our research article "Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View" (Zhang et al., 2017) [1]. More detail about other used data and our findings can be found in Zhang et al. (2017) [1].

Across Europe, Greenways upcycle disused railway infrastructure into non-motorised public infrastructure, often with limited consideration to potential ecological synergies. Pre-development, disused transport corridors become relatively undisturbed and potentially host diverse semi-natural habitats. The study objectives were 1) to produce a highly detailed and accurate dataset using remote sensing with rapid assessment techniques for ground truthing and 2) subsequently examine habitat diversity existing along a proposed Greenway. A 7000 ha study corridor was based on a disused railway proposed as a transfrontier Greenway connecting the Republic of Ireland and the United Kingdom. The study applied a rapid-assessment virtual validation techniquealongside remote sensing and accuracy assessment. Inter-relationship between seminatural habitat diversity and land-use intensification was examined. Remote sensing accuracies of 89% and 99% for a real and linear habitat classification were obtained. Degrees of land-use intensification were observed throughout the corridor, highlighting the importance of maintaining and enhancing remaining semi-natural habitat that exists along the proposed Greenway route. Through understanding the landscape matrix composition and semi-natural habitat diversity, European Greenwayscan achieve multi-functionality for ecosystem conservation, forming integral components of Green Infrastructure.

The recovery of a built heritage and specifically of singular buildings is a key aspect of local development. The aim of this study was to understand the influence of these regenerations on their environment by transforming adjacent businesses and initiating parallel processes of gentrification and local development. The renewed attraction of these new businesses to the area can result in increased employment and production. The methodology used was based on self-organizing maps of neural networks with matrix architecture and competitive learning. Through the analysis of neural networks, we were able to identify common relationships and behaviors in commercial properties which are adjacent to singular buildings and that share common patterns and characteristics or attributes. The singular buildings analyzed are located along the Spanish Mediterranean coast in the cities of Almería, Barcelona, and Valencia. The results obtained were based on the following hypotheses: occupancy model and the classification based on total occupancy, total variation in occupancy, and the most common types of usage of a given ground floor commercial property. Among the conclusions, we highlight the existence of commercial premises that display anti-cyclical economic behavior and the presence of commercial premises considered to be “unfortunate” or with low potential.

Abstract Background The aim was to examine inter-rater and alternate-form reliability of the Microscale Audit of Pedestrian Streetscapes (MAPS) Global tool to assess the physical environment along likely walking routes in Belgium. Methods For 65 children participating in the BEPAS-children study, routes between their individual homes and the nearest pre-defined destination were defined. Using MAPS Global, physical environmental characteristics of the routes were audited by 4 trained auditors (2 on-site, 2 online using Google Street View). Inter-rater reliability was studied for on-site and online ratings separately. Alternate-form reliability was examined by comparing on-site with online ratings. Results Inter-rater reliability for on-site ratings was acceptable for 68% of items (kappa range 0.03–1.00) and for online ratings for 60% of items (kappa range −0.03 to 1.00). Acceptable alternate-form reliability was reported for 60% of items (kappa range −0.01 to 1.00/r range 0.31–1.00). Conclusions MAPS Global can be used to assess the physical environment of potential walking routes. For areas where Google Street View imagery is widely covered and often updated, MAPS Global can be completed online.

Las urbanizaciones cerradas son realidades cada vez más recurrentes en el paisaje urbano español. Aunque su atención científica ha estado centrada tradicionalmente en Madrid, este trabajo pretende profundizar en el conocimiento de estas fórmulas de segregación urbana en otros ámbitos de España, concretamente en las aglomeraciones de Valencia y Sevilla. La localización de estas urbanizaciones así como su análisis territorial y tipológico son los principales objetivos de esta investigación. Desde una perspectiva metodológica proponemos la aplicación Street View como base para la localización de estos complejos. Se comprueba que las urbanizaciones cerradas más ajustadas a las definiciones convencionales conviven con otras fórmulas de cerramiento en el medio rural (Sevilla) y en ámbitos turísticos del litoral (Valencia), cada una con lógicas territoriales propias.

Abstract Background Alcohol-related harm is a major global health issue, and controls on alcohol marketing are one intervention utilized by governments. This study investigated the use of Google Street View (GSV) as a novel research method for collecting alcohol-related data in the urban environment. Methods The efficacy of GSV and on-street observation by observer teams was compared by surveying 400 m stretches of 12 streets in Wellington, the capital city of New Zealand. Data on alcohol sale, alcohol-related advertising, health promotion materials, regulatory information and visible alcohol consumption were collected. Results A total of 403 retailers with evidence of alcohol sales and 1161 items of alcohol-related communication were identified in on-street observation. Of the latter, 1028 items (89 %) were for alcohol marketing and 133 (11 %) were for alcohol-related health promotion and alcohol regulation. GSV was found to be a less sensitive tool than on-street observation with only 50 % of the alcohol venues identified and 52 % of the venue-associated brand marketing identified. A high degree of inter-observer reliability was generally found between pairs of observers e.g., for the detection of alcohol retail venues the intra-class correlation coefficient (ICC) was 0.93 (95 % CI: 0.78 to 0.98) for on-street observation and 0.85 (95 % CI: 0.49 to 0.96) for using GSV. Conclusions GSV does not seem suitable for the comprehensive study of the influences on alcohol consumption in the urban streetscape. However, it may still have value for large, static objects in the environment and be more time efficient than traditional on-street observation measures, especially when used to collect data across a wide geographical area. Furthermore, GSV might become a more useful research tool in settings with better image quality (such as more ‘footpath views’) and with more regularly updated GSV imagery.

Building databases are important assets when estimating and planning for national energy savings from energy retrofitting. However, databases often lack information on building characteristics needed to determine the feasibility of specific energy conservation measures. In this paper, machine learning methods are used to enrich the Swedish database of Energy Performance Certificates with building characteristics relevant for a chosen set of energy retrofitting packages. The study is limited to the Swedish multifamily building stock constructed between 1945 and 1975, as these buildings are facing refurbishment needs that advantageously can be combined with energy retrofitting. In total, 514 ocular observations were conducted in Google Street View of two building characteristics that were needed to determine the feasibility of the chosen energy retrofitting packages: (i) building type and (ii) suitability for additional façade insulation. Results showed that these building characteristics could be predicted with an accuracy of 88.9% and 72.5% respectively. It could be concluded that machine learning methods show promising potential to enrich building databases with building characteristics relevant for energy retrofitting, which in turn can improve estimations of national energy savings potential.

Researchers have been looking for easily accessible and straightforwardly useable paradata and auxiliary data to improve survey data. Lately, there is also attention for the evaluation and validation of this external data, such as the assessment of the quality of interviewer-generated paradata. For these purposes, we investigated how useful Google Street View can be as auxiliary data and whether it allows us to assess the quality of interviewer observations on the houses and neighbourhoods of sample units. Additionally, we test whether Google Maps can inform us about the reachability of sample units and which data can predict non-response better. Although it is rather simple to use Google Maps and Street View in daily life, using it to code auxiliary data for surveys is more challenging than expected. Hence, this paper also offers a thorough discussion of the pitfalls of coding these auxiliary data as well as their current solutions.

Introduction Although there is global growth in outdoor smokefree areas, little is known about the associated smokefree signage. We aimed to study smokefree signage at playgrounds and to compare field observations with images from Google Street View (GSV). Material and Methods We randomly selected playgrounds in 21 contiguous local government areas in the lower North Island of New Zealand, all of which had smokefree playground policies. Field data were collected on smokefree signage along with dog control signage to allow for comparisons. The sensitivity and specificity of using GSV for data collection were calculated. Results Out of the 63 playgrounds studied, only 44% (95% CI: 33%–57%) had any smokefree signage within 10 m of the playground equipment. The mean number of such signs was 0.8 per playground (range: 0 to 6). Sign size varied greatly from 42 cm2 up to 2880 cm2; but was typically fairly small (median = 600 cm2; ie, as per a 20 × 30 cm rectangle). Qualitatively the dog signs appeared to use clearer images and were less wordy than the smokefree signs. Most playground equipment (82%), could be seen on GSV, but for these settings the sensitivity for identifying smokefree signs was poor at 16%. Yet specificity was reasonable at 96%. Conclusions The presence and quality of smokefree signage was poor in this sample of children’s playgrounds in this developed country setting. There appears to be value in comparing smokefree signage with other types of signage (eg, dog control signage). Google Street View was not a sensitive tool for studying such signage.

(1) Background: Evidence-based policymaking requires data about the local population’s socioeconomic status (SES) at detailed geographical level, however, such information is often not available, or is too expensive to acquire. Researchers have proposed solutions to estimate SES indicators by analyzing Google Street View images, however, these methods are also resource-intensive, since they require large volumes of manually labeled training data. (2) Methods: We propose a methodology for automatically computing surrogate variables of SES indicators using street images of parked cars and deep multiple instance learning. Our approach does not require any manually created labels, apart from data already available by statistical authorities, while the entire pipeline for image acquisition, parked car detection, car classification, and surrogate variable computation is fully automated. The proposed surrogate variables are then used in linear regression models to estimate the target SES indicators. (3) Results: We implement and evaluate a model based on the proposed surrogate variable at 30 municipalities of varying SES in Greece. Our model has R 2 = 0.76 and a correlation coefficient of 0.874 with the true unemployment rate, while it achieves a mean absolute percentage error of 0.089 and mean absolute error of 1.87 on a held-out test set. Similar results are also obtained for other socioeconomic indicators, related to education level and occupational prestige. (4) Conclusions: The proposed methodology can be used to estimate SES indicators at the local level automatically, using images of parked cars detected via Google Street View, without the need for any manual labeling effort.

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.

Hemispherical (fisheye) photography is a well-established approach for estimating the sky view factor (SVF). High-resolution urban models from LiDAR and oblique airborne photogrammetry can provide continuous SVF estimates over a large urban area, but such data are not always available and are difficult to acquire. Street view panoramas have become widely available in urban areas worldwide: Google Street View (GSV) maintains a global network of panoramas excluding China and several other countries; Baidu Street View (BSV) and Tencent Street View (TSV) focus their panorama acquisition efforts within China, and have covered hundreds of cities therein. In this paper, we approach this issue from a big data perspective by presenting and validating a method for automatic estimation of SVF from massive amounts of street view photographs. Comparisons were made with SVF estimates derived from two independent sources: a LiDAR-based Digital Surface Model (DSM) and an oblique airborne photogrammetry-based 3D city model (OAP3D), resulting in a correlation coefficient of 0.863 and 0.987, respectively. The comparisons demonstrated the capacity of the proposed method to provide reliable SVF estimates. Additionally, we present an application of the proposed method with about 12,000 GSV panoramas to characterize the spatial distribution of SVF over Manhattan Island in New York City. Although this is a proof-of-concept study, it has shown the potential of the proposed approach to assist urban climate and urban planning research. However, further development is needed before this approach can be finally delivered to the urban climate and urban planning communities for practical applications.

O presente trabalho teve como objetivo analisar e mostrar a importância de um Plano de Arborização para a cidade de Presidente Epitácio (SP). Utilizou-se como metodologia de levantamento dos dados o uso da ferramenta do Google (Google Street View), com imagens realizadas em 2011 em 68 ruas, sendo as mesmas divididas em par e ímpar. Em função de comparação do ano de 2011 para 2014, um levantamento in loco em 2014 também foi realizado, utilizando o método de avaliação em campo. Os dados foram analisados através de teste t pareado comparando a densidade de árvores por sítio de amostragem. Em termos absolutos observou-se que em cada sítio, ouve um aumento na densidade arbórea entre os anos, embora estatisticamente não significativo. Esse aumento pode ser considerado pela influencia de ações de educação ambiental, através de programas de plantio de árvores realizados nos últimos anos.

Molti servizi di cartografia online – Google Maps, Bing Maps, Nokia Maps, Yandex Maps e altri ancora – permettono di esplorare alcuni luoghi da un punto di vista quasi in soggettiva, come se l'utente si trovasse effettivamente sul posto.

Sistemi Avanzati presents three innovative solutions for mobile mapping Earthmine, the metric evolution of Google Street View, uses a stereoscopic panoramic sensor and 3D vision algorithm to generates 360 degrees images where any point is geo-tagged with its 3D coordinates. Imajing is a portable, easy to use, low cost, video mapping system combining high resolution camera, IMU, barometer, control unit, user friendly software, that enables the final user to directly perform his road survey campaign. Ike1000 is the world’s first truly integrated data-capture-device that combines GPS with camera, 3D compass, rangefinder, allowing remote GPS measurement and acquisition of images geotagged (lat, long and alt) on the target coordinates.

Sistemi Avanzati presents three innovative solutions for mobile mapping Earthmine, the metric evolution of Google Street View, uses a stereoscopic panoramic sensor and 3D vision algorithm to generates 360 degrees images where any point is geo-tagged with its 3D coordinates. Imajing is a portable, easy to use, low cost, video mapping system combining high resolution camera, IMU, barometer, control unit, user friendly software, that enables the final user to directly perform his road survey campaign. Ike1000 is the world’s first truly integrated data-capture-device that combines GPS with camera, 3D compass, rangefinder, allowing remote GPS measurement and acquisition of images geotagged (lat, long and alt) on the target coordinates.