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In this paper, we investigate the potential of a Conditional Random Field (CRF) approach for the classification of an airborne LiDAR\ud(Light Detection And Ranging) point cloud. This method enables the incorporation of contextual information and learning of specific\udrelations of object classes within a training step. Thus, it is a powerful approach for obtaining reliable results even in complex urban\udscenes. Geometrical features as well as an intensity value are used to distinguish the five object classes building, low vegetation, tree,\udnatural ground, and asphalt ground. The performance of our method is evaluated on the dataset of Vaihingen, Germany, in the context\udof the 'ISPRS Test Project on Urban Classification and 3D Building Reconstruction'. Therefore, the results of the 3D classification\udwere submitted as a 2D binary label image for a subset of two classes, namely building and tree

CONDITIONAL RANDOM FIELDS FOR LIDAR POINT CLOUD CLASSIFICATION IN COMPLEX URBAN AREAS