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Combining publicly available Google Street View (GSV) images with deep learning and radiative transfer models enables researchers to assess urban physical and thermal environments, especially in high-density areas. This book introduces this advanced approach, which provides large-scale, high-accuracy, and high-density measurements of street-level urban features key to understanding urban radiation balance and thermal dynamics.
By leveraging GSV images, this method directly characterizes urban streetscapes, including structural and geometric attributes, allowing for a comprehensive assessment. Moreover, the methods can be applied to any geographical location covered by GSV, making it a low-cost and effective tool for urban studies globally. This is particularly advantageous compared to traditional 3D-GIS models, which may not always be freely available or as extensive in coverage as GSV images.
Lessons from Street Sensing provide data-driven insights for urban planning and governance. The accurate mapping of street view factors and solar irradiance helps identify areas with insufficient greenery, excessive or insufficient solar exposure, and other urban environment issues. These insights can help policymakers and urban planners mitigate negative environmental impacts and improve urban living conditions.
Sommario
Chapter 1: Introduction.- Chapter 2: Theoretical background.- Chapter 3: Methodology.- Chapter 4: Spatial Patterns of Street Canyon View Factors.- Chapter 5: Spatiotemporal Patterns of Street Canyon Solar Radiation.- Chapter 6: Implementation of Urban Planning and Design at Street Level.- Chapter 7: Conclusions.
