Measuring Perceptions of Walkable Streetscapes in Cultural Heritage Contexts

This study examines pedestrian perceptions of streetscapes in Isfahan’s cultural heritage site by integrating deep learning–based image segmentation with urban morphological analysis. Using a U-Net model applied to First-Person Pedestrian View (FPPV) images, five perceptual indices (imageability, enclosure, human scale, greenness, and walking index) were quantified to assess their influence on pedestrian experience. Street width was explicitly incorporated as a morphological variable to examine its relationship with perceptual qualities using spearman correlation analysis and visual trend analysis using Pearson correlation. The results reveal consistent relationships between visual composition and perceptual outcomes, particularly strong associations between imageability, enclosure, and vegetation structure, as well as trade-offs between enclosure and sky visibility. In contrast, variables such as human scale and walking index show weak or negligible associations with street width, suggesting that pedestrian presence and activity patterns in heritage contexts are more strongly influenced by landscape elements, water features, and spatial continuity than by dimensional factors alone. Findings highlight how urban renewal strategies, such as streetscape enhancement and cultural preservation, shape pedestrian movement and spatial perception. Segmentation-based analysis achieved an accuracy of 83% in classifying dominant streetscape elements, offering a robust alternative to traditional survey-based methods. This study contributes a data-driven framework for assessing pedestrian streetscapes, emphasizing morphological continuity, human-scale design, and green infrastructure as critical determinants of walkability. It also identifies key challenges, including fragmented spatial morphology and inconsistent urban furniture placement, which affect pedestrian comfort and use of space. These findings support evidence-based policy and design strategies for optimizing historic urban streetscapes, with implications for balancing heritage conservation and modern pedestrian needs. Future research may refine perceptual metrics and extend the approach across diverse urban contexts.