Screening the Restorative Visual Potential of University Classroom Window Views: An AI-Assisted Semantic and Depth Analysis Framework

Classroom window views are important components of the visual environment in university buildings, yet campus-scale assessment remains largely manual. This study developed an AI-assisted method for the relative screening of outdoor visual content in classroom window views by integrating DeepLabV3+ semantic segmentation with MiDaS monocular relative depth estimation. The dataset comprised 812 standardized window view images from four university buildings in Nanjing, China. Five visual proxy metrics—Visible Vegetation Ratio, Framed Sky Visibility, Relative Visual Depth, Hard Facade Ratio, and Trunk Exposure Ratio—were combined into a Composite Screening Score (CSS). The segmentation model achieved a mean intersection over union of 0.76 on an independent test set. In a range-stratified sample of 80 images, CSS was strongly associated with the mean ratings of five experts judging restorative visual potential from the photographs (Spearman’s ρ = 0.76, p < 0.001). The method supports interpretable relative ranking, metric-specific diagnosis, preliminary screening, and prioritization of on-site inspections in university building management.