Urban neighbourhood environment assessment based on street view image processing: A review of research trends

He, Nan; Li, Guanghao

doi:10.1016/j.envc.2021.100090

Cited by 61 publications

(24 citation statements)

References 63 publications

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“…The Google Street View Images (SVIs) are used for analysis of the visual perspective of the users assessed through this study. Subjective perception has been examined frequently in research related to urban studies, where data collection in most cases have been carried out with person-to-person interactions [47]. But recently the use of SVIs in research has been more popular as it saves both time and resources [16,32,[48][49][50].…”

Section: Methodsmentioning

confidence: 99%

“…But recently the use of SVIs in research has been more popular as it saves both time and resources [16,32,[48][49][50]. Further, SVI assessments are always free from noise disturbance or any discomfort from temperature, humidity or wind, making it ideal for visual perception assessments [47]. It has been recommended to use SVIs for the studies on psychological state of the respondents and the characteristics of physical elements [51,52].…”

Section: Methodsmentioning

confidence: 99%

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The impact of visual complexity on perceived safety and comfort of the users: A study on urban streetscape of Sri Lanka

2022

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Increase in the variety of development in urban context has made it more complicated and complex for the users of public spaces. Absence of sufficient information to read the surrounding causes psychological anxiousness leading to perceived danger or discomfort for the urbanites. Consequently, perceived safety and comfort of the users is distinctively low in urban contexts, creating neglected and underused spaces. Complexity is one of the information processing variables as per Kaplan and Kaplan’s informational model which helps users to comprehend the surrounding environment. The streetscape plays a vital role in the daily movement patterns within the urban cities and is the transition boundary between the public and private realms. Visual complexity of these streets is a result of different configurations of elements within the urban areas. This research is conducted to ascertain the relationship between visual complexity levels of the streets with the perceived safety and comfort of the users. Shannon Diversity Index (SDI) and Fractal dimension analysis were conducted with 48 SVIs (Street View Images) selected within 1km radius of Colpetty junction, Colombo Sri Lanka covering all the possible compositions found within the context. The visual index data extraction had identified ten major components within the selected 48 SVIs. 78 subjective ranking responses for perceived safety, comfort (preference) and perceived complexity were collected from snowball sampling. Findings of the study revealed that perceived safety levels and preference scores for the SVIs are related to the Shannon Diversity Index calculation in an inverted ‘U’ shape where the highest and lowest SDI values are related with low preference scores and low safety levels. The SVIs with medium SDI values are perceived as the safest and most preferred by the users of urban streets of Colombo Sri Lanka. The SDI and fractal dimension values were significantly correlated with the perceived complexity scores of the users. The results of this study can be accommodated in the planning and designing of urban streetscapes of tropical climates for sustainable and friendly urban expansions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The impact of visual complexity on perceived safety and comfort of the users: A study on urban streetscape of Sri Lanka

2022

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“…Over the past eight years, approximately 34% of studies analyzing neighborhood environmental impressions used urban street view [24]. The walkability of streets is used to evaluate city infrastructure and amenities, and streetscape photography can quantify landscape characteristics.…”

Section: Emerging Research Methods Help Street Environment Measurementmentioning

confidence: 99%

Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China

Tao

Wang

et al. 2022

Land

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Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and the visual characteristics of the streetscape. The image-segmentation model DeepLabv3+ automatically extracts each pixel’s semantic information and classifies visual elements from 120,012 collected panoramic street view images of Zhengzhou, China, using the entropy weighting method and weighted superposition to calculate the street perception summary score. This deep learning approach can successfully describe the semantics of streets and the connection between population density and street perception. The study provides a new quantitative method for urban planning and the development of high-density cities.

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“…In recent years, many researchers have begun to use street-view images combined with deep learning algorithms to investigate the elements that most strongly affect urban street perception ( 30 , 31 ). Deep learning algorithms, such as FCN, Resnet, and SegNet, use a deep convolutional neural network to process visual information within images, leading to an accurate identification of various visual features that include lanes, buildings, the sky, sidewalks, trees, and greenery and laying a solid foundation for better research on the quality of urban streets and human perception ( 34 – 36 ). Ordonez and Berg ( 37 ) collected a street-view image dataset from four cities and used deep learning models to explore a joint model of wealth, uniqueness, and perceived safety on a city scale.…”

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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Urban neighbourhood environment assessment based on street view image processing: A review of research trends

Cited by 61 publications

References 63 publications

The impact of visual complexity on perceived safety and comfort of the users: A study on urban streetscape of Sri Lanka

The impact of visual complexity on perceived safety and comfort of the users: A study on urban streetscape of Sri Lanka

Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China

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

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