EXTRACTING B UILDING FOOTPRINTS FROM 3D POINT CLOUDS USING TERRESTRIAL LASER SCANNING AT STREET LEVEL

In this paper, we address the problem of generating building footprints using terrestrial laser scanning from a Mobile Mapping System (MMS). The MMS constitutes a fast and adapted tool to extract precise data for 3D city modeling. Urban environments evolve over time due to human activities and other factors. Buildings are constructed or destroyed and the urban areas are extended. Therefore, the structures of the cities are constantly modified. Currently, building footprints can be generated using aerial data. However, aerial based footprints lack precision due to the nature of the data and to the associated extraction methods. The use of MMS is proposed as an alternative to perform this complex task. In this work, we propose an operational approach for automatic extraction of accurate building footprints. We describe the challenges associated with the terrestrial laser raw data acquired in realistic and dense urban environments. After a filtering stage on the 3D laser cloud point, we extract and reconstruct the dominant facade planes by combining the Hough transform, the k-means clustering algorithm and the RANSAC method. The building footprint is then estimated from these dominant planes. Preliminary experimental results are presented and discussed. The assessments show that this approach is very promising for the automation of building footprints extraction.