  In the area of computer vision, deep learning techniques have recently been
used to predict whether urban scenes are likely to be considered beautiful: it
turns out that these techniques are able to make accurate predictions. Yet they
fall short when it comes to generating actionable insights for urban design. To
support urban interventions, one needs to go beyond predicting beauty, and
tackle the challenge of recreating beauty. Unfortunately, deep learning
techniques have not been designed with that challenge in mind. Given their
"black-box nature", these models cannot be directly used to explain why a
particular urban scene is deemed to be beautiful. To partly fix that, we
propose a deep learning framework called Facelift, that is able to both
beautify existing urban scenes (Google Street views) and explain which urban
elements make those transformed scenes beautiful. To quantitatively evaluate
our framework, we cannot resort to any existing metric (as the research problem
at hand has never been tackled before) and need to formulate new ones. These
new metrics should ideally capture the presence/absence of elements that make
urban spaces great. Upon a review of the urban planning literature, we identify
five main metrics: walkability, green spaces, openness, landmarks and visual
complexity. We find that, across all the five metrics, the beautified scenes
meet the expectations set by the literature on what great spaces tend to be
made of. This result is further confirmed by a 20-participant expert survey in
which FaceLift have been found to be effective in promoting citizen
participation. All this suggests that, in the future, as our framework's
components are further researched and become better and more sophisticated, it
is not hard to imagine technologies that will be able to accurately and
efficiently support architects and planners in the design of spaces we
intuitively love.
