Heterogeneity Study of the Visual Features Based on Geographically Weighted Principal Components Analysis Applied to an Urban Community

Liu, Yong; Yang, Shizhong; Wang, Shijun

doi:10.3390/su132313488

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Kang et al [8] also conduct a comprehensive review on the utilization of street view images for sensing urban environments, discussing the associated methodologies for image processing and semantic understanding. In a similar vein, Liu et al [27] employ semantic segmentation techniques to analyze street view images. Wang et al [28] employed deep learning through semantic segmentation in conjunction with space syntax to gauge residents' perceptions of city streets.…”

Section: Applying Street Images and Deep-learning Technique For Urban...mentioning

confidence: 99%

Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images

Iamtrakul,

Chayphong,

Kantavat

et al. 2024

Sustainability

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Recently, deep learning techniques, specifically semantic segmentation, have been employed to extract visual features from street images, a dimension that has received limited attention in the investigation of the connection between subjective and objective road environment perception. This study is dedicated to exploring and comprehending the factors influencing commuters’ perceptions of the road environment, with the aim of bridging the gap in interpreting environmental quality in Thailand. Semantic segmentation was applied to identify visual objects, expressed as a percentage of pixels represented in 14,812 street images from the Bangkok Metropolitan Region. Subjective road environment perception was assessed through a questionnaire, with a total of 3600 samples collected. Both sets of data were converted to average values per grid, with a grid size of 500 × 500 square meters, resulting in a total of 631 grids with data points. Finally, a multiple linear regression model was employed to analyze the relationship between the ratios of objects obtained from street images via semantic segmentation and human sensory perception of the road environment. The findings from this analysis indicate that the attributes of distinct object classes have a notable impact on individuals’ perceptions of the road environment. Visual elements such as infrastructure, construction, nature, and vehicles were identified as influential factors in shaping the perception of the road environment. However, human and object features did not exhibit statistical significance in this regard. Furthermore, when examining different road environments, which can be categorized into urban, community, and rural contexts, it becomes evident that these contexts distinctly affect the perceptions of various road environments. Consequently, gaining a comprehensive understanding of how street environments are perceived is crucial for the design and planning of neighborhoods and urban communities, facilitating the creation of safer and more enjoyable living environments.

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Section: Applying Street Images and Deep-learning Technique For Urban...mentioning

confidence: 99%

Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images

Iamtrakul,

Chayphong,

Kantavat

et al. 2024

Sustainability

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“…However, the development of existing sightseeing routes is typically based on the macro perspective [30], and visual perception research based on tourists is relatively lacking. In terms of visual perception, the factors affecting the landscape quality of the water perspective are distinct to those of the waterfront perspective [31], and the visual characteristics under different scales have obvious spatial heterogeneity [32]. However, research on the differences between on-water sightseeing landscape aesthetics across multiple scales of urban rivers is currently lacking.…”

Section: Introductionmentioning

confidence: 99%

Effects of River Scale on the Aesthetic Quality of Urban On-Water Sightseeing

Wang

Han

et al. 2022

Sustainability

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On-water sightseeing plays a key role in the tourism of traditional Chinese landscapes. The on-water landscape affects tourism potential and the quality of urban landscapes. Current research on river landscape is mainly based on remote sensing images or on-land approaches, while studies of on-water perspective landscapes at different river scales is lacking. In this paper, with Guilin city rivers taken as an example, we adopt image semantic segmentation technology to evaluate the visual landscape characteristics under different river scales, and subsequently employ automatic linear modeling to screen important factors affecting aesthetic quality. The results reveal the obvious differences between the on-water landscape characteristics of different scale rivers. The on-water landscape quality of large- and small-scale rivers is mainly affected by seven and four factors, respectively. The Karst landform of Guilin is observed to significantly improve the on-water landscape quality of large-scale rivers. By considering the impact mechanism of landscape composition on the aesthetic quality and the different scale rivers, we propose several aesthetic quality improvement strategies based on low-cost methods, including the planting of vegetation and the micro renewal of artificial constructions. This study contributes to the intelligent evaluation of urban on-water landscape and provides reference for on-water route selection and urban planning.

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Heterogeneity Study of the Visual Features Based on Geographically Weighted Principal Components Analysis Applied to an Urban Community

Cited by 2 publications

References 20 publications

Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images

Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images

Effects of River Scale on the Aesthetic Quality of Urban On-Water Sightseeing

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