Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning

Li, Yunqin; Yabuki, Nobuyoshi; Fukuda, Tomohiro

doi:10.1016/j.scs.2022.104140

Cited by 41 publications

(27 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Road, car, sidewalk, and person significantly positively affected safety scores. This result is fully consistent with the proposition to improve urban vitality and safety [30,72], and is also highly consistent with the results obtained from other safety perception studies conducted in Chinese cities [5,8,73,74]. Rail and fences also significantly and positively affect safety scores; this result corresponds to the management of contemporary urban planning.…”

Section: Factors Affecting Subjective Safety Perceptionsupporting

confidence: 91%

Women-oriented Evaluation of Perceived Safety of Walking Routes Between Home and Mass Transit: A Case Study and Methodology Test in Guangzhou

Cui¹,

Gong²,

Yang³

et al. 2023

Preprint

View full text Add to dashboard Cite

Streets are an essential element of urban safety governance and urban design, but they are designed with little regard for possible gender differences. This study proposes a safety perception evaluation method from the female perspective based on street view images (SVIs) and mobile phone data, taking the central city of Guangzhou as an example. The method relies on crowdsourced data and uses a machine learning model to predict the safety perception map. It combines the simulation of women's walking commuting paths to analyse the areas that need to be prioritised for improvement. Multiple linear regression was used to explain the relationship between safety perception and visual elements. The results showed the following: 1) There were differences in safety perceptions across genders. Women gave overall lower safety scores and more dispersed distribution of scores. 2) Approximately 11% of the streets in the study area showed weak perceived safety, and approximately 3% of these streets have high pedestrian flows and require priority improvements. 3) Safe visual elements in SVIs included the existence of roads, sidewalks, cars, railways, people, skyscrapers, and trees. Our findings can help urban designers determine how to evaluate urban safety and where to optimise key areas. Both have practical implications for urban planners seeking to create urban environments that promote greater safety.

show abstract

Section: Factors Affecting Subjective Safety Perceptionsupporting

confidence: 91%

Women-oriented Evaluation of Perceived Safety of Walking Routes Between Home and Mass Transit: A Case Study and Methodology Test in Guangzhou

Cui¹,

Gong²,

Yang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The open street enhances the impression of safety (Montgomery 2014), it potentially encourages walking activities for residents (Arnold 1980, Cauwenberf et al (2012). In addition, the movement factors in the street, such as motorized vehicles and non-motorized vehicles, can affect pedestrians' sense of safety and comfort in the street space, thus affecting the pedestrian walking experience (Li et al 2022a(Li et al , 2022b. Related studies have gradually incorporated micro-scale and subjective-level walkability criteria into walkability evaluations (Bivina andParida 2019, Howell et al 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, related studies have proposed visual walkability indices to evaluate visual walkability. Li et al (2022aLi et al ( , 2022b described visual walkability perception as whether the built environment of a street visually encourages people to walk, emphasizing the influence of visual elements on walk willingness Li et al (2022aLi et al ( , 2022b.…”

Section: Literature Reviewmentioning

confidence: 99%

Using google street view panoramas to investigate the influence of urban coastal street environment on visual walkability

Huang

Lyu

et al. 2023

Environ. Res. Commun.

View full text Add to dashboard Cite

Urban street walkability can effectively promote public health and the construction of livable cities. In addition, the coastal streets play a positive role in showing urban vitality and image. Due to the growing leisure needs of residents, measuring the visual walkability perception (VIWP) in urban streets and exploring the influence mechanisms of urban coastal street environments on VIWP have theoretical and practical significance. However, the methods of the previous walkability studies have limitations in terms of cost, time and measurement scale. Based on Google Street View Panoramic (GSVP) image data, this study used the semantic difference (SD) method with virtual reality (VR) technology to evaluate the VIWP of Fukuoka coastal streets. Meanwhile, the proportion of streetscape elements was extracted from GSVP images by semantic segmentation. The correlation and regression analyses were performed between the VIWP evaluation values and streetscape elements. Then, the regression model of the VIWP and the streetscape elements was established. The results showed that the natural features had a positive influence on VIWP in coastal streets. Correspondingly, trees were the strongest contribution rate for the VIWP, followed by shrubs, grasses and water, however, buildings and cars had a negative influence on VIWP. The method extends previous studies for measuring walkability, and optimization strategies were proposed to improve the visual quality of the coastal streets. It can be applied in the construction and management of walkable coastal street environments.

show abstract

“…Generative Adversarial Networks (GAN) have been utilized for simulating urban patterns [38,39]. Contributions from deep learning (DL) and machine learning (ML) methods to the evolution of models in various aspects of prediction, planning, and uncertainty analysis in smart cities and urban development have been notable [40,41], providing support for urban planning and decision making [42].…”

Section: Application Of Machine Learning In Urban Perceptionmentioning

confidence: 99%

Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception

Zhang

et al. 2023

Land

Self Cite

View full text Add to dashboard Cite

Urban spatial perception critically influences human behavior and emotional reactions, emphasizing the necessity of aligning urban spaces with human needs for enhanced urban living. However, functionality-based categorization of urban architecture is prone to biases, stemming from disparities between objective mapping and subjective perception. These biases can result in urban planning and designs that fail to cater adequately to the needs and preferences of city residents, negatively impacting their quality of life and the city’s overall functionality. This research scrutinizes the perceptual biases and disparities in architectural function distribution within urban spaces, with a particular focus on Shanghai’s central urban district. The study employs machine learning to clarify these biases within urban spatial perception research, utilizing a tripartite methodology: objective mapping, subjective perception analysis, and perception deviation assessment. The study revealed significant discrepancies in the distribution centroids between commercial buildings and residential or public buildings. This result illuminates the spatial organization characteristics of urban architectural functions, serving as a valuable reference for urban planning and development. Furthermore, it uncovers the advantages and disadvantages of different data sources and techniques in interpreting urban spatial perception, paving the way for a more comprehensive understanding of the subject. Our findings underscore the need for urban planning strategies that align with human perceptual needs, thereby enhancing the quality of the urban environment and fostering a more habitable and sustainable urban space. The study’s implications suggest that a deeper understanding of perceptual needs can optimize architectural function distribution, enhancing the urban environment’s quality.

show abstract

Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning

Cited by 41 publications

References 53 publications

Women-oriented Evaluation of Perceived Safety of Walking Routes Between Home and Mass Transit: A Case Study and Methodology Test in Guangzhou

Women-oriented Evaluation of Perceived Safety of Walking Routes Between Home and Mass Transit: A Case Study and Methodology Test in Guangzhou

Using google street view panoramas to investigate the influence of urban coastal street environment on visual walkability

Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception

Contact Info

Product

Resources

About