Combining human perception and street accessibility to provide information for better street construction: a case study of Chengdu City, China

High-quality street spaces have a positive impact on residents’ lives. Revealing the differences in human perception and walkability across street spaces can highlight priority areas for street quality enhancement and provide valuable insights that help urban managers make informed decisions. This study first utilised machine learning models (SegNet) to extract visual elements from street view images and collected volunteers’ ratings of six perceptual dimensions of street scenes. Subsequently, it employed random forest algorithms and spatial autocorrelation models to predict and analyse overall street perception. Furthermore, principal component analysis was used to consolidate the six perceptual scores, coupled with an analysis of street walkability, to identify streets that most required quality enhancement. Finally, correlation and regression analyses were conducted between human perception ratings and street visual elements. The results indicated that buildings positively influence perceptions of wealth, safety, liveliness, and beauty and are the most significant positive perception elements. Conversely, walls were the most significant negative perception elements. Additionally, trees positively impacted perceptions of liveliness and beauty. Lastly, streets categorised as “high walkable-low perception” exhibited a radial dispersion from the city centre. In conclusion, the findings of this study provide valuable insights for enhancing street-space quality in Chengdu and other cities.