This study proposes a workable approach for quantitatively measuring the perceptual-based visual quality of streets, which has often relied on subjective impressions or feelings. With the help of recently emerged street view images and machine learning algorithms, an evaluation model has been trained to assess the perceived visual quality with accuracy similar to that of experienced urban designers, to provide full coverage and detailed results for a citywide area. The town centre of Shanghai was selected for the site. Around 140,000 screenshots from Baidu Street View were processed and a machine learning algorithm, SegNet, was applied to intelligently extract the pixels representing key elements affecting the visual quality of streets, including the building frontage, greenery, sky view, pedestrian space, motorisation, and diversity. A Java-based program was then produced to automatically collect the preferences of experienced urban

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