Can 2D Remote Sensing Coverage Represent Residents’ Perceived Visual Green? A Street View Deep Learning Analysis for Refined Urban Green Planning

Accurate greenspace quantification underpins sustainable urban greening management. Remote sensing (RS)-derived green quantity is a core urban planning indicator, yet its capability to reflect actual urban green supply lacks systematic verification, inevitably affecting planning formulation and decision-making. Taking Shanghai as the study area, this study integrates remote sensing and Baidu Street View (BSV data) to compare the two-dimensional planar green coverage derived from satellite imagery with pedestrian-level perceived visual green coverage. The Mask2Former model was adopted for high-precision semantic segmentation of BSV images to extract vegetation, building, sky and hard pavement proportions. Geographically Weighted Regression (GWR), hotspot analysis and transition mapping were applied to identify divergent regions, while the XGBoost-SHAP framework was employed to explore deviation mechanisms. The results reveal distinct spatial pattern differences between the two green quantity datasets. RS-derived green quantity exhibits strip-like agglomeration, whereas BSV-perceived green quantity is more fragmented, with a correlation coefficient of only 0.240. Single RS quantification fails to reflect street-level green supply. Divergent areas are classified into accurate, overestimated and underestimated zones. RS underestimates green quantity in central urban areas and overestimates that in northwest suburbs. BSV-based sky ratio, building ratio, hardscape ratio and Road 1 density are core influencing factors with obvious nonlinear threshold effects. This study clarifies the quantitative deviation patterns and mechanisms between RS and BSV green quantity, providing scientific support for precise urban green planning and sustainable perceived visual green construction. Practically, the dual RS–street view assessment framework proposed in this paper can be embedded into routine urban green infrastructure auditing, help planners distinguish systematically overestimated suburban green belts and underestimated central urban micro-green spaces, and deliver targeted optimization strategies for vertical greening, street tree renovation and pocket park construction under high-quality urban renewal demands.