Performance of machine learning models.

Campus walking environments significantly influence college students' daily lives and shape their subjective perceptions. However, previous studies have been constrained by limited sample sizes and inefficient, time-consuming methodologies. To address these limitations, we developed a deep learning framework to evaluate campus walking perceptions across four universities in China's Yangtze River Delta region. Utilizing 15,596 Baidu Street View Images (BSVIs), and perceptual ratings from 100 volunteers across four dimensions—aesthetics, security, depression, and vitality—we employed four machine learning models to predict perceptual scores. Our results demonstrate that the Random Forest (RF) model outperformed others in predicting aesthetics, security, and vitality, while linear regression was most effective for depression. Spatial analysis revealed that perceptions of aesthetics, security, and vitality were concentrated in landmark areas and regions with high pedestrian flow. Multiple linear regression analysis indicated that buildings exhibited stronger correlations with depression (β = 0.112) compared to other perceptual aspects. Moreover, vegetation (β = 0.032) and meadows (β = 0.176) elements significantly enhanced aesthetics. This study offers actionable insights for optimizing campus walking environments from a student-centered perspective, emphasizing the importance of spatial design and visual elements in enhancing students' perceptual experiences.

校园步行环境对大学生的日常生活具有显著影响，并塑造其主观感知体验。然而，过往研究受限于样本规模有限、方法低效且耗时的缺陷。为弥补这些不足，本研究构建了深度学习框架，用于评估中国长三角地区四所高校的校园步行感知体验。本研究使用了15596幅百度街景图像（Baidu Street View Images，BSVIs），以及100名志愿者针对美学、安全性、压抑感、活力感四个维度给出的感知评分，并采用四种机器学习模型预测感知得分。研究结果显示，随机森林（Random Forest，RF）模型在美学、安全性与活力感的预测任务中表现优于其他模型，而线性回归则在压抑感的预测上效果最佳。空间分析表明，美学、安全性与活力感的感知评分集中分布在地标区域及行人流量较高的区域。多元线性回归分析显示，与其他感知维度相比，建筑与压抑感的相关性更强（β=0.112）。此外，植被（β=0.032）与草坪（β=0.176）元素可显著提升美学感知评分。本研究从以学生为中心的视角出发，为优化校园步行环境提供了可落地的实践思路，并强调了空间设计与视觉元素对提升学生感知体验的重要性。