Technologie du génie architectural

Abstrait

Automatic Extraction of 3D Objects from LiDAR Data

Abdelmounaim Bellakaout*, Cherkaoui Omari Mohammed, Ettarid Mohamed, Touzani Abderrahmane

Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain, it is becoming one of the important tools in the geosciences for studying earth surface. Classification of LiDAR data for the purpose of extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, remote sensing, geographical information system (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, anautomatic process is greatly required to handle the immense amounts of data collected because the manual process is long and expensive. This paper presents an approach for automatic classification of aerial LiDAR data into 5 groups– buildings, trees, roads, linear object and soil using single return LIDAR and processing the point cloud without generating DEM. Topological relationship and height variation analysis is adopted to segment the entire point cloud preliminarily into upper contour, lower contour, uniform surface, non-uniform surface, linear objects, and the rest. This primary classification is used on the one hand to know the upper and lower of each building in urban scene needed to model façade building and on the second hand to extract point cloud of uniform surface which contain roof, road and ground used in the second phase of classification. The second algorithm is developed to segment the uniform surface into roof building, road and ground, the second phase of classification based on the topological relationship and height variation analysis, The proposed approach has been tested using two areas the first is a housing complex and the second is a primary school. The proposed approach follows in this study proves successful classification results of buildings, vegetation and road classes.