StreetGen : In base city scale procedural generation of streets: road network, road surface and street objects

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 (∼ cm 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).