Street greening is a popular topic in urban design research. Traditionally, assessments for urban greening levels using Normalised Difference Vegetation Index (NDVI) from satellite remote sensing images, often overlooking street greening from a human-scale perspective. This study combined spatial syntax, machine learning techniques, streetscape images, and remote sensing data to comprehensively assess thoroughly analyse street greening levels in Chengdu's Fourth Ring Road. Additionally, by integrating accessibility analysis with Green View Index (GVI), this study identified areas that should be prioritised for street greening interventions. The results indicate that: (1) Streets in the western and southern regions of Chengdu City's Fourth Ring Road possessed higher GVI. (2) There is a significant difference in the overall distributions of GVI and NDVI, particularly in the central and eastern regions. (3) Streets with “high commuting and walking accessibility (low GVI) overlapped in the area east of Shuncheng Avenue. The methodology presented in this study can serve as a reference for human-scale street greening in Chengdu and other cities.