  Knowledge of the spatial organisation of economic activity within a city is
key to policy concerns. However, in developing cities with high levels of
informality, this information is often unavailable. Recent progress in machine
learning together with the availability of street imagery offers an affordable
and easily automated solution. Here we propose an algorithm that can detect
what we call 'visible firms' using street view imagery. Using Medell\'in,
Colombia as a case study, we illustrate how this approach can be used to
uncover previously unseen economic activity. Applying spatial analysis to our
dataset we detect a polycentric structure with five distinct clusters located
in both the established centre and peripheral areas. Comparing the density of
visible and registered firms, we find that informal activity concentrates in
poor but densely populated areas. Our findings highlight the large gap between
what is captured in official data and the reality on the ground.
