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Given the size of modern cities in the urbanising age, it is beyond theperceptual capacity of most people to develop a good knowledge about the beautyand ugliness of the city at every street corner. Correspondingly, for planners,it is also difficult to accurately answer questions like 'where are theworst-looking places in the city that regeneration should give firstconsideration', or 'in the fast urbanising cities, how is the city appearancechanging', etc. To address this issue, we here present a computer vision methodfor the large-scale and automatic evaluation of the urban visual environment,by leveraging state-of-the-art machine learning techniques and thewide-coverage street view images. From the various factors that are at work, wechoose two key features, the visual quality of street facade and the continuityof street wall, as the starting point of this line of analysis. In order totest the validity of this method, we further compare the machine ratings withratings collected on site from 752 passers-by on fifty-six locations. We showthat the machine learning model can produce a good estimation of people's realvisual experience, and it holds much potential for various tasks in terms ofurban design evaluation, culture identification, etc.

A machine learning method for the large-scale evaluation of urban visual environment