  Cities are structured by roads. Having up to date and detailed maps of these
is thus an important challenge for urban planing, civil engineering and
transportation. Those maps are traditionally created manually, which represents
a massive amount of work, and may discard recent or temporary changes. For
these reasons, automated map building has been a long time goal, either for
road network reconstruction or for local road surface reconstruction from low
level observations. In this work, we navigate between these two goals. Starting
from an approximate road axis (+ width) network as a simple road modelling, we
propose to use observations of street features and optimisation to improve the
coarse model. Observations are generic, and as such, can be derived from
various data, such as aerial images, street images and street Lidar, other GIS
data, and complementary user input.
  Starting from an initial road modelling which is at a median distance of 1.5
metre from the sidewalk ground-truth, our method has the potential to robustly
optimise the road modelling so the median distance reaches 0.45 metre fully
automatically, with better results possible using user inputs and/or more
precise observations. The robust non linear least square optimisation used is
extremely fast, with computing time from few minutes (whole Paris) to less than
a second for a few blocks.
  The proposed approach is simple, very fast and produces a credible road
model. These promising results open the way to various applications, such as
integration in an interactive framework, or integration in a more powerful
optimisation method, which would be able to further segment road network and
use more complex road model.
