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

Xueshun Li, Kuntong Huang, Ruinan Zhang, Yang Chen () and Yu Dong ()
Additional contact information
Xueshun Li: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
Kuntong Huang: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
Ruinan Zhang: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
Yang Chen: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
Yu Dong: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China

Land, 2024, vol. 13, issue 3, 1-33

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.

Keywords: visual perception optimization; residential landscape space; immersive virtual environment; cold regions; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/3/367/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/3/367/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:3:p:367-:d:1356943

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().