Objective  The relationship between visible street greenery from the humanistic perspective (an indicator of refined perceptual quality) and green coverage from the bird’s-eye view that is commonly employed in planning and control practice is explored to provide theoretical and practical support for the effective incorporation of visible street greenery into green urban design and planning.

Methods  Data are collected, and green coverage, visible street greenery, and potential influencing factors are calculated. Each sample’s green coverage is estimated using satellite images and GIS to calculate their vegetation cover with the normalized difference vegetation index (NDVI). Additionally, deep learning algorithms are used to extract green areas from streetscape big data and calculate them through GIS. Urban morphology data are obtained by processing big data through ArcGIS, natural conditions data are obtained from www.tianqi.com, and economic-level data are obtained from relevant statistical yearbooks. Moreover, the correlation between visible street greenery and greenery coverage in different areas is discussed by a four-quadrant classification method: consistent visible street greenery and green coverage (visible street greenery and green coverage are both high or both low, category A); high green coverage while low visible street greenery (category B); high visible street greenery while low green coverage (category C). A four-quadrant multiple logistic regression is used to analyze the impact of potential influencing factors on the formation of the aforesaid three categories of relationships between visible street greenery and green coverage. Finally, multiple linear regression is used to analyze the influence of each potential factor on visible street greenery.

Results  The economic level may influence the performance of both visible street greenery and green coverage. Generally, first-tier and new first-tier cities exhibited consistent visible street greenery and greenery coverage, while second-tier cities displayed inconsistencies. The multiple logistic regression reveals a significant and positive influence of economic level on the consistency between visible street greenery and green coverage. GDP per capita shows a significant positive correlation with category A and category C, as higher economic level and higher financial investment can enhance the city residents’ perception of green resources. Category B is proportional to the distance from the township urban center. Urban form can also influence the performance of visible street greenery and green coverage, but high-rise buildings do not affect the transformation of green resources into street greenery resources. Therefore, in addition to natural climatic conditions, visible street greenery is also positively influenced by greenery coverage and economic level, and can reflect the extent of a city’s street greening and off-street greening. High greenery coverage can facilitate the achievement of high visible street greenery, which is negatively influenced by block size.

Conclusion  A city’s green coverage guiding does not guarantee visible street greenery. Therefore, a humanistic perspective on visible street greenery should be included as a guiding factor in the progressive development of green urban design. Neither the consistency between visible street greenery and green coverage nor the specific level of the visible street greenery indicator itself can be determined by natural climatic conditions alone, and moderate financial investment can effectively improve both the possibility of consistency and visible street greenery. Finally, urban design should adopt small streets and dense roads, and urban peripheral areas should create street greenery rather than concentrated green areas.