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Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning

Author

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  • Xueshun Li

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

  • Kuntong Huang

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

  • Ruinan Zhang

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

  • Yang Chen

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

  • Yu Dong

    (School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150001, China)

Abstract

The visual perception of landscape spaces between residences in cold regions is important for public health. To compensate for the existing research ignoring the cold snow season’s influence, this study selected two types of outdoor landscape space environments in non-snow and snow seasons as research objects. An eye tracker combined with a semantic differential (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out the gaze characteristics in the landscape space, and reveal the design factors related to landscape visual perception. In the snow season, the spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlations with the landscape visual perception scores (W). In the non-snow season, in addition to the above three factors, the roof height difference (RHD), tall-tree height (TTH), and hue contrast (HC) also markedly influenced W. The effects of factors on W were revealed in immersive virtual environment (IVE) orthogonal experiments, and the genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimize the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82–2.15, RHD: 10.81–20.09 m, BS: 48.53–61.01, TTH: 14.18–18.29 m, GP: 0.12–0.15, and HC: 18.64–26.83. In the snow season environment, the optimized threshold ranges were SAR: 2.22–2.54, BS: 68.47–82.34, and GP: 0.1–0.14.

Suggested Citation

  • Xueshun Li & Kuntong Huang & Ruinan Zhang & Yang Chen & Yu Dong, 2024. "Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning," Land, MDPI, vol. 13(3), pages 1-33, March.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:3:p:367-:d:1356943
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    References listed on IDEAS

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    1. Andrea Abraham & Kathrin Sommerhalder & Thomas Abel, 2010. "Landscape and well-being: a scoping study on the health-promoting impact of outdoor environments," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 55(1), pages 59-69, February.
    2. Jaewon Han & Sugie Lee, 2023. "Verification of Immersive Virtual Reality as a Streetscape Evaluation Method in Urban Residential Areas," Land, MDPI, vol. 12(2), pages 1-16, January.
    3. Zijiao Zhang & Kangfu Zhuo & Wenhan Wei & Fu Li & Jie Yin & Liyan Xu, 2021. "Emotional Responses to the Visual Patterns of Urban Streets: Evidence from Physiological and Subjective Indicators," IJERPH, MDPI, vol. 18(18), pages 1-20, September.
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