Version 1
: Received: 24 December 2023 / Approved: 26 December 2023 / Online: 26 December 2023 (07:48:19 CET)
How to cite:
Li, X.; Huang, K.; Zhang, R.; Chen, Y.; Dong, Y. Visual Perception Optimization of Residential Leisure Space in Cold Regions Using Virtual Reality and Machine Learning. Preprints2023, 2023121941. https://doi.org/10.20944/preprints202312.1941.v1
Li, X.; Huang, K.; Zhang, R.; Chen, Y.; Dong, Y. Visual Perception Optimization of Residential Leisure Space in Cold Regions Using Virtual Reality and Machine Learning. Preprints 2023, 2023121941. https://doi.org/10.20944/preprints202312.1941.v1
Li, X.; Huang, K.; Zhang, R.; Chen, Y.; Dong, Y. Visual Perception Optimization of Residential Leisure Space in Cold Regions Using Virtual Reality and Machine Learning. Preprints2023, 2023121941. https://doi.org/10.20944/preprints202312.1941.v1
APA Style
Li, X., Huang, K., Zhang, R., Chen, Y., & Dong, Y. (2023). Visual Perception Optimization of Residential Leisure Space in Cold Regions Using Virtual Reality and Machine Learning. Preprints. https://doi.org/10.20944/preprints202312.1941.v1
Chicago/Turabian Style
Li, X., Yang Chen and Yu Dong. 2023 "Visual Perception Optimization of Residential Leisure Space in Cold Regions Using Virtual Reality and Machine Learning" Preprints. https://doi.org/10.20944/preprints202312.1941.v1
Abstract
The visual perception of leisure spaces between residences in cold regions is important for public health. To compensate for the existing researches` lack of ignoring the cold snow season`s influence, this study selected two types of outdoor leisure space environments in non-snow and snow seasons as research objects. An eye tracker combined with a Semantic Difference (SD) questionnaire was used to verify the feasibility of the application of virtual reality technology, screen out leisure gaze characteristics, and reveal the environmental factors related to leisure visual perception. In snow season, spatial aspect ratio (SAR), building elevation saturation (BS), and grass proportion in the field of view (GP) showed strong correlation to the leisure visual perception scores (W). In non-snow season, apart from the above three factors, roof height difference (RHD), tall tree height (TTH) and color contrast (HC) also influenced W obviously. The effects of factors on W were revealed in immersive virtual en-vironment (IVE) orthogonal experiments and genetic algorithm (GA) and k-nearest neighbor algorithm (KNN) were combined to optimized the environmental factors. The optimized threshold ranges in the non-snow season environment were SAR: 1.82‒2.15, RHD: 10.81 m‒20.09 m, BS: 48.53‒61.01, TTH: 14.18 m‒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.
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
