Estimating Street-Level Green View Index Using Satellite Remote Sensing and Explainable Machine Learning

Street-level greenery assessment through the Green View Index (GVI) is essential for transportation planning and sustainable urban development. However, current GVI estimation methods rely heavily on street-view imagery, facing significant scalability challenges including high data acquisition costs, inconsistent temporal coverage, and limited cross-regional comparability. This study proposes an explainable machine learning framework for GVI estimation using ubiquitous satellite remote sensing data from Sentinel-2A imagery. Eight complementary spectral indices were systematically evaluated: enhanced vegetation indices, spectral variation indices, and urban context indices. Multiple spatial buffer configurations (200m to 1000m) and input strategies were tested, including mean-value aggregation and raster input for CNN models. The methodology was validated using over 6,300 samples from Helsinki (Finland), with comprehensive comparisons across traditional machine learning models and deep learning architectures.The optimized XGBoost model with 1000m buffer achieved R2 = 0.67 and MSE = 0.022, improved approximately 20% over baseline approaches using only NDVI and RGB bands. SHAP (SHapley Additive exPlanations) analysis revealed spatial scale-dependent feature importance patterns, with Urban Index (UI) consistently ranking highest across all scales, while NDVI showed lower importance at larger buffer sizes. Alternative spatial modeling approaches (CNN) underperformed compared to mean-value aggregation methods, suggesting that statistical aggregation may be more robust for GVI predictions. The framework eliminates dependency on street-view imagery while maintaining competitive accuracy, offering a scalable solution for large-scale green infrastructure assessment. The explainable AI approach provides interpretable insights into environmental factors influencing street-level green perception, supporting evidence-based decision-making in transportation planning and active transportation network development.