Commission VI, WG VI/4 KEY WORDS: LiDAR, accuracy, quality control, error Assessment. ABSTRACT:Most of LiDAR systems do not provide the end user with the calibration and acquisition procedures that can use to validate the quality of the data acquired by the airborne system. Therefore, this system needs data Quality Control (QC) and assessment procedures to verify the accuracy of the laser footprints and mainly at building edges. This research paper introduces an efficient method for validating the quality of the airborne LiDAR point clouds data using terrestrial laser scanning data integrated with edge detection techniques. This method will be based on detecting the edge of buildings from these two independent systems. Hence, the building edges are extracted from the airborne data using an algorithm that is based on the standard deviation of neighbour point's height from certain threshold with respect to centre points using radius threshold. The algorithm is adaptive to different point densities. The approach is combined with another innovative edge detection technique from terrestrial laser scanning point clouds that is based on the height and point density constraints. Finally, statistical analysis and assessment will be applied to compare these two systems in term of edge detection extraction precision, which will be a priori step for 3D city modelling generated from heterogeneous LiDAR systems.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

ABSTRACT:The classification of different objects in the urban area using airborne LIDAR point clouds is a challenging problem especially with low density data. This problem is even more complicated if RGB information is not available with the point clouds. The aim of this paper is to present a framework for the classification of the low density LIDAR data in urban area with the objective to identify buildings, vehicles, trees and roads, without the use of RGB information. The approach is based on several steps, from the extraction of above the ground objects, classification using PCA, computing the NDSM and intensity analysis, for which a correction strategy was developed. The airborne LIDAR data used to test the research framework are of low density ( 2 / 41 . 1 m pts ) and were taken over an urban area in San Diego, California, USA. The results showed that the proposed framework is efficient and robust for the classification of objects.

In this work we are focusing of the registration of Long Range scans taken in the SAG mode, we find an automatic registration algorithm based on mapping 3D scans to 2D images. After mapping the scans to images, we use SURF matching algorithm for automatic matching points detection. Rough registration parameters can be estimated via Least Squares method based on the tie points in the two scans. To refine these registration parameters, the Iterative Closest Point (ICP) approach is used with the scans. The overall method of automation of registration is efficient in terms of speed, and accuracy, especially with many scans.

In this paper a synergy scheme between aerial imagery and sparse LIDAR point clouds is proposed for an automated aerial image classification. In this scheme, a point cloud and an image are chosen for a certain urban area. The point cloud is automatically classified into buildings, vegetation and roads using PCA and intensity variation. Afterwards, a projection of the point cloud into an image is obtained, such that it is registered with the aerial image. The aerial image classifier is trained with the LIDAR classification result to generate an automated classifier for aerial images. The classifier is tested with another image to demonstrate its accuracy. Another benefit of the synergy proposed is to densify the planar patches of the low density point cloud using the segmented aerial image to help modelling applications achieve more precise boundaries.