Shao Yuan, Ye Dan, Ye Yu

Keywords： Urban Design; Street; Interface Transparency; Deep Learning; Street View Image; Human-scale

Abstract：

Accompanying with the delicacy transformation of urban planning and design, humanscale street qualities have been regarded as key issues in recent years. Street transparency refers to the percentage of the area of the street door and window openings to the area of the street interface. Nevertheless, existed studies mainly rely on manual-based analytical approaches with high-cost and lowefficiency, which is hard to be measured on the city scale. As a response to this issue, this paper proposes a set of large-scale and refined measurement and analysis methods for street transparency based on the integration of street view images and deep learning. Taking Shanghai as an example, this paper completes the calculation and visualization of street interface transparency effectively. The verification via manual labeling obtains high coefficients with automatically computed results in statistical analysis, which proves the validity of this study. The empirical analysis finds that there is obvious spatial heterogeneity in street transparency in the central Shanghai, showing a spatial pattern of “high inside and low outside”. This paper is a result of integrating classical urban design concerns with new data and new techniques, which helps support human-oriented urban design practice; and it reveals a big picture co-presenting large-scale and high-precision results, which helps designers to seek in-depth understandings in this direction.

Funds：

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