This chapter first highlights the summary of the contributions of this book. Then, it summarizes the strengths and assumptions of the street sensing approach in this study. Finally, it outlines the limitations and potential future works. This chapter consolidates the methodological innovations of a GSV-driven street sensing framework for urban climatic studies. By leveraging deep-learning and hemispheric photography, the approach accurately quantifies sky (SVF), tree (TVF), and building (BVF) view factors, revealing Hong Kong’s high-density urban morphology dominated by low greenery and obstructed skies. Seasonal solar radiation mapping identifies winter under-exposure in shaded canyons and summer over-exposure in open zones, driven by street orientation and geometry. Strengths include global applicability via ubiquitous street view platforms and low-cost scalability. Key assumptions—stable subtropical tree cover, spatial irradiance homogeneity, and zero tree transmissivity—are justified but flagged for refinement. Limitations like ignored reflected radiation and reliance on local observatory data are discussed, with proposed solutions involving satellite integration and radiative transfer modeling. Future work emphasizes global comparative studies, dynamic sky luminance integration, and urban microclimate parameterization. This framework bridges street-level environmental assessment with sustainable urban design, offering policymakers tools to enhance thermal comfort and solar equity in high-density cities.