This chapter presents a novel approach to quantify sky view factor (SVF), tree view factor (TVF), and building view factor (BVF) in Hong Kong’s high-density urban environment using Google Street View (GSV) imagery and a deep-learning-based scene parsing algorithm. The method addresses the complexity of urban geometry, including building overhangs and tree canopy cover, by analyzing 29,264 GSV images at 30-m intervals. Validation against hemispheric photography field measurements confirms high accuracy (R2 > 0.95 for SVF, TVF, and BVF), marking the first direct verification of GSV-derived view factors using fisheye lens data. A comparative analysis with conventional 3D-GIS modeling reveals significant overestimation of SVF by the 3D-GIS method (average difference: 0.11), attributed to its inability to account for street tree obstruction. Spatial mapping demonstrates that high-density areas exhibit lower SVF (0.49 average) and TVF (0.14 average), with tree coverage strongly correlated to discrepancies between GSV and 3D-GIS results (R2 = 0.53). The study highlights the superiority of GSV-based methods in capturing ground-level urban complexity and underscores the necessity of integrating street tree data into urban radiative models for improved microclimate assessments.