  Streets are large, diverse, and used for several (and possibly conflicting)
transport modalities as well as social and cultural activities. Proper planning
is essential and requires data. Manually fabricating data that represent
streets (street reconstruction) is error-prone and time consuming. Automatising
street reconstruction is a challenge because of the diversity, size, and scale
of the details (few centimetres for cornerstone) required. The state-of-the-art
focuses on roads (no context, no urban features) and is strongly determined by
each application (simulation, visualisation, planning). We propose a unified
framework that works on real Geographic Information System (GIS) data and uses
a strong, yet simple modelling hypothesis when possible to robustly model
streets at the city level or street level. Our method produces a coherent
street-network model containing topological traffic information, road surface
and street objects. We demonstrate the robustness and genericity of our method
by reconstructing the entire city of Paris streets and exploring other similar
reconstruction (airport driveway).
