Visual Perception Experience of Campus Street Walking Space Based on Gradient-weighted Class Activation Mapping
This study focuses on the pedestrian spaces of campus streets at a university in southern China,utilizing Gradient-weighted Class Activation Mapping(Grad-CAM)to explore pedestrian visual perception experiences.Grad-CAM intuitively highlights key areas in street images that affect pedestrian comfort,automatically identifying and emphasizing visual elements such as vegetation and vehicles,and revealing their impact on the pedestrian experience and visual perception.By comparing Grad-CAM activation maps with eye-tracking data,and incorporating analyses from SHAP and kernel density estimation models,the study summarizes the main street characteristics that shape pedestrian perception,and uncovers the diverse effects of streetscape elements on visual perception.This study provides architects with an analytical method that is easy to understand and apply for the refined design of campus pedestrian spaces.
campus street spacestreet walkabilityvisual perceptual experiencedeep learningClass Activation Mappinginterpretability
万方数据
维普
