Ensemble Learning with Multi-Source Data Fusion for Modeling and Gap-Filling of Streetscape Greenery: An Application to Shichahai, Beijing

In high-density built environments, traditional single-source street-level data are often limited by spatial blind spots when measuring the Green View Index (GVI). Street-view images (SVIs) have been widely used for extracting GVI, but their coverage is often discontinuous. To achieve a more continuous estimation of GVI in complex urban blocks, this study proposes an estimation framework that combines SVI-derived GVI extraction with ensemble learning and multi-source geospatial data. Shichahai Subdistrict in Beijing is used as a case study. Spatial morphology and vegetation-related factors are quantified within multi-scale buffers, and a Stacking ensemble model combining Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) is used to infer missing GVI values. The Stacking model achieved the best overall validation performance among the tested models, with an MAE of 0.0651 and an R2 of 0.81, outperforming the single models and simpler benchmark models. Environmental factors also exhibited scale sensitivity, with vegetation factors showing stronger explanatory power at the 25 m micro-scale and building morphology factors showing negative associations at larger scales. In this case study, the framework mitigates discontinuities caused by gaps in street-view data and provides quantitative evidence for refined green renewal in high-density historic urban areas, but the results should be interpreted as site-specific interpolation rather than general cross-city prediction.