ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 409-416, 2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
20 Aug 2015
R. Cura, J. Perret, and N. Paparoditis Universite Paris-Est, IGN, SRIG, COGIT & MATIS, 73 Avenue de Paris, 94160 Saint Mande, France
Keywords: StreetGen, Street Modeling, RDBMS, Road Network, GIS database, kinetic hypothesys, Variable Buffer Abstract. Streets are large, diverse, and used for 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 and is strongly oriented 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 hypothesis when possible to produce coherent street modelling at the city scale or street scale. Because it is updated only locally in subsequent computing, the result can be improved by adapting input data and the parameters of the model. We reconstruct the entire Paris streets in a few minutes and show how the results can be edited simultaneously by several concurrent users.
Conference paper (PDF, 16776 KB)

Citation: Cura, R., Perret, J., and Paparoditis, N.: STREETGEN: IN-BASE PROCEDURAL-BASED ROAD GENERATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 409-416, doi:10.5194/isprsannals-II-3-W5-409-2015, 2015.

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