Street view imagery in urban analytics and GIS: A review

Biljecki, Filip; Ito, Koichi

doi:10.1016/j.landurbplan.2021.104217

Cited by 372 publications

(115 citation statements)

References 233 publications

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“…Many international mapping services have made street-view data accessible to users through an API. The most popular mainstream mapping services used in research are Google Maps, Tencent Maps, and Baidu Maps ( 26 – 28 ). As interactive electronic maps, street views provide users with a panoramic view of the urban built environment at a low cost with high accessibility, high resolution, and wide coverage; thus, street-view images have become a very important new data source for urban built environment research ( 29 – 31 ).…”

Section: Related Workmentioning

confidence: 99%

Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning

Han

Wang

Seo

et al. 2022

Front. Public Health

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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.

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Section: Related Workmentioning

confidence: 99%

Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning

Han

Wang

Seo

et al. 2022

Front. Public Health

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“…Baidu Map is an online map service provided by Baidu [40]. The panoramic static image service provided by Baidu Map (https://map.baidu.com, accessed on 31 January 2022) is similar to GSV.…”

Section: Baidu Street View Imagesmentioning

confidence: 99%

Exploring the Impact of Built Environment Attributes on Social Followings Using Social Media Data and Deep Learning

Tang

Zhang

Liu

et al. 2022

IJGI

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Streets are an important component of urban landscapes and reflect the image, quality of life, and vitality of public spaces. With the help of the Google Cityscapes urban dataset and the DeepLab-v3 deep learning model, we segmented panoramic images to obtain visual statistics, and analyzed the impact of built environment attributes on a restaurant’s popularity. The results show that restaurant reviews are affected by the density of traffic signs, flow of pedestrians, the bicycle slow-moving index, and variations in the terrain, among which the density of traffic signs has a significant negative correlation with the number of reviews. The most critical factor that affects ratings on restaurants’ food, indoor environment and service is pedestrian flow, followed by road walkability and bicycle slow-moving index, and then natural elements (sky openness, greening rate, and terrain), traffic-related factors (road network density and motor vehicle interference index), and artificial environment (such as the building rate), while people’s willingness to stay has a significant negative effect on ratings. The qualities of the built environment that affect per capita consumption include density of traffic signs, pedestrian flow, and degree of non-motorized design, where the density of traffic signs has the most significant effect.

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“…With the rapid development of information and communication technologies, analysis of increasingly accessible geo-referenced urban data offers a quantitative human-centred approach to capturing the social dynamics of public space. Recent research focuses on the detection of user behaviours and routes based on different types of activity information and Big Data analysis (Biljecki & Ito, 2021;Chen et al, 2016). One category of research, which involves computer visionbased systems to detect and track pedestrians, has been fast developing due to the number of possible applications such as crowd size measurement, transport security, pedestrian traffic management, etc.…”

Section: Spatial-temporal Public Space Analysismentioning

confidence: 99%

UAV-based People Location Tracking and Analysis for the Data-Driven Assessment of Social Activities in Public Spaces

Ameijde¹,

Leung²

2022

CAADRIA Proceedings

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In sustainable high-density cities, public spaces play an important role in supporting social and community health and wellbeing. Amidst ongoing urbanisation, it is of increasing importance to study public space interaction patterns and placemaking processes that contribute to the quality of life of urban residents. This paper reports on the development of a new methodology for the computational tracking and analysis of social activities in urban spaces, using Computer Vision Object Detection (CVOD) techniques to create digitalised pedestrian trajectory data. Referring to concepts from humanistic geography and time geography, our method offers a new platform for data-driven urban place studies, detecting co-presence and social interaction in relation to urban morphology. This paper focuses on the development of Machine Learning protocols, algorithms for tracing and mapping pedestrian trajectories in a georeferenced photogrammetry model, and computational analysis of co-presence. The resulting workflow forms a foundation for future research around detecting, analysing and quantifying behavioural parameters, to evaluate the ability of public spaces to support social interaction and placemaking.

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Street view imagery in urban analytics and GIS: A review

Cited by 372 publications

References 233 publications

Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning

Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning

Exploring the Impact of Built Environment Attributes on Social Followings Using Social Media Data and Deep Learning

UAV-based People Location Tracking and Analysis for the Data-Driven Assessment of Social Activities in Public Spaces

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