With the rapid improvement of LIDAR systems regarding point density and accuracy in relation to the (application dependent) requirements, robustness, efficiency and automation of the modeling process are becoming more important than achieving the highest possible accuracy and modeling detail from the available LIDAR data. Therefore we opt for development of a 2D grid based LIDAR data analysis approach. An important step is detection and parameterization of planar surfaces (roof elements). The paper reviews four methods, based on analysis of gradients, principal components, least squares and hough transforms, respectively. It introduces a series of improvements to the standard usage of each of those methods and shows results from synthetic and real data.