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.

中文翻译：

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利用虚拟现实和机器学习的寒冷地区居住景观空间视觉感知优化

寒冷地区住宅之间景观空间的视觉感知对于公众健康非常重要。为了弥补现有研究忽视冷雪季影响的不足，本研究选取非雪季和雪季两类室外景观空间环境作为研究对象。采用眼动仪结合语义差异（SD）问卷来验证虚拟现实技术应用的可行性，筛选出景观空间中的注视特征，揭示与景观视觉感知相关的设计因素。在雪季，空间纵横比（SAR）、建筑物高程饱和度（BS）和视野中的草地比例（GP）与景观视觉感知得分（W）表现出很强的相关性。非雪季，除上述三个因素外，屋顶高差（RHD）、高树高度（TTH）和色调对比度（HC）也对W产生显着影响。揭示了因素对W的影响在沉浸式虚拟环境（IVE）正交实验中，结合遗传算法（GA）和k近邻算法（KNN）对环境因素进行优化。非雪季环境下的优化阈值范围为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、HC：18.64~26.83 。雪季环境下，优化阈值范围为SAR：2.22~2.54、BS：68.47~82.34、GP：0.1~0.14。