Deep Learning-Based Aesthetic Perception of Spring Zone Street View Images: A Case Study of Jinan Mingfu City Area

Lin Chen, Li Liu, Zhe Liu

To address the existing gap in quantitative evaluation regarding the integrated visual effect of spring water landscapes and street spaces within Historical and Cultural Neighborhoods of Spring Zone, the Jinan Mingfu City area is selected as a typical case for this research. A quantitative framework for street view aesthetic perception based on deep learning is constructed. ResNet-101 predicts aesthetic scores using 6796 Street View Images (SVIs), while DeepLabV3+ extracts visual elements. A four-dimensional design quality system is established, including Traffic Safety, Street Vitality, Spatial Comfort, and Living Convenience. Clustering and regression analysis reveal the relationship between aesthetic perception and design quality. Results show: (1) the Water Blue View Index (WBVI) is the primary predictor of street aesthetic scores (β = 0.419), confirming the visual dominance of the “spring water” element; (2) WBVI, Green View Index (GVI), and Sky View Factor (SVF) constitute a “Natural Perception Triangle (NPT)” as the ecological comfort foundation; (3) streets are categorized into six types with significant design quality variations; (4) the proposed “Street Perception Optimization Matrix (SPOM)” provides differentiated renewal strategies for Spring Zone. This research presents a framework for visual quality assessment and refined renewal of Historical and Cultural Neighborhoods of Spring Zone.