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    基于VR全景图技术的乡村景观视觉评价偏好研究

    Study on the visual evaluation preference of rural landscape based on VR panorama.

    • 摘要: 乡村景观评价是乡村景观规划的基础。为了更好地了解和研究乡村景观视觉环境的影响因素,本文首次将VR全景图技术应用于景观评价的研究中,进行了新技术应用于风景园林领域的实践尝试。利用VR全景图技术对空间、场景高度还原仿真的特点,改进传统的SBE法,以城市青年人群为例,通过观测实验并进行打分,从而得出原始数据,对乡村景观视觉评价及景观要素偏好进行研究。首先,使用改进后的SBE法与SD法构建了乡村景观视觉评价模型,并确定了4个显著影响指标(建筑肌理感、植物种类、道路形态和卫生状况)。其次,根据VR全景图场景中偏好视域截图,进而从截图中归纳提取出9个景观偏好指标。利用线性回归的方法,分别研究这9个景观偏好指标的偏好概率与该指标价值分数、所在场景SBE得分之间是否存在函数关系,并得出三者之间互相作用的模型。得出建筑特色指标与水环境指标的偏好概率与其自身的价值分数有显著关系;建筑肌理感、农田肌理感的偏好概率受其价值分数与场景SBE值共同影响;自然视野与其他视觉焦点的偏好概率受到场景SBE值影响更为显著;道路形态、道路材料和植物种类偏好概率与其自身价值分数、场景SBE值并无明显关系。最后,分析出城市青年人群会产生此种偏好的原因,并希望能够对未来乡村景观建设起到指导作用。

       

      Abstract: Evaluation of rural landscape is the foundation of rural landscape planning. In order to better understand the influencing factors on rural landscape visual environment, the research uses VR panorama technology in the field of landscape architecture for the first time. VR panorama technology could record and simulate scene and space much better than traditional ways. Therefore, this paper uses VR panorama technology, taking the city young people as example, and then the original data was obtained in visual preference by doing the experiment in watching VR panorama. In this way, we improved the traditional SBE method and studied the rural landscape visual assessment and landscape element preference. Firstly, the visual evaluation model of rural landscape was constructed by using SBE method and SD method, and 4 significant indexes were identified as follows: architecture texture, plant species, road shape and hygiene status. Then according to the VR panorama, we made interviewers shoot at their prefer view, and then based on these preference pictures, we extracted 9 landscape preferences. By using linear regression method, we established model of the relationship among preference probability of the nine landscape indexes, the value of index, and SBE value of the scene to test whether there was a functional relationship among the three variables. Then we got the results: preference probability of architecture characteristics and the water quality only had significant relationship with their own value; the preference probability of architecture texture and farmland texture were impacted by their value and the scene SBE value; the preference probability of nature vision and other visual focus were influenced by scene SBE value; road shape, road materials and plant species preference probability had no significant relationship with either its own fractional values or the scene SBE value. Finally, the reasons for this preference were analyzed, and hope it will play a guiding role in the construction of rural landscape in the future.

       

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