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