Abstract:Historic street spaces not only serve material functions but also directly influence people's experiential and psychological perceptions. This study examines Guilin's historic streets, which face ageing infrastructure, declining landscape quality, and reduced vitality. A combined subjective-objective approach was adopted, employing street-view image segmentation and machine learning to quantify spatial quality and human perception. Partial Least Squares regression analysis was used to explore relationships between visual elements and perceptual responses. Results show that human perceptions exhibit spatial heterogeneity: beauty and richness are focused within core scenic areas, liveliness is prominent along pedestrian streets and main roads, and boredom and oppression are common in older alleys. Spatial indicators display systematic patterns: green view index increases from the core to periphery, openness is higher at public nodes and main roads, enclosure is concentrated in traditional alleys, walkability is locally strong but generally weak, and motorization follows a 'high on main roads, low in blocks' pattern. Perception-space relationships are complex: green view, openness, enclosure, walkability, and motorization exert differing positive and negative effects on perceptions of beauty, liveliness, and oppression. Finally, based on the research findings, micro-renewal optimization strategies for Guilin's historic districts are proposed.
