Traffic signs are one of the most relevant road assets for driving, as the safety of drivers depends to a great extent on their correct location. In this paper two methods are compared for the segmentation of the sign and the pole supporting it. Both methods are based on the morphological opening to identify the sign points, the first one directly employs the mathematical morphology directly applied to point clouds and the second one through point cloud rasterization into images. The comparison was conducted on twenty real traffic signs acquired with Mobile Laser Scanning obtaining point clouds from environments with signposts, traffic lights and lampposts. The results showed a correct segmentation of the signs, obtaining a F-score of 0.81 by the point-based method and a 0.75 by 2D image method. In particular, the point-based mathematical morphology proved to be more accurate in the segmentation of traffic sings installed on traffic lights and lampposts, avoiding over detection shown by the 2D image method.

During the last couple of years, there has been an increased interest to develop new deep learning networks specifically for processing 3D point cloud data. In that context, this work intends to expand the applicability of one of these networks, PointNet, from the semantic segmentation of indoor scenes, to outdoor point clouds acquired with Airborne Laser Scanning (ALS) systems. Our goal is to of assist the classification of future iterations of a national wide dataset such as the Actueel Hoogtebestand Nederland (AHN), using a classification model trained with a previous iteration. First, a simple application such as ground classification is proposed in order to prove the capabilities of the proposed deep learning architecture to perform an efficient point-wise classification with aerial point clouds. Then, two different models based on PointNet are defined to classify the most relevant elements in the case study data: Ground, vegetation and buildings. While the model for ground classification performs with a F-score metric above 96%, motivating the second part of the work, the overall accuracy of the remaining models is around 87%, showing consistency across different versions of AHN but with improvable false positive and false negative rates. Therefore, this work concludes that the proposed classification of future AHN iterations is feasible but needs more experimentation.

In present work it is pretended to estimate elastic parameters of beams through the combined use of precision geomatic techniques (laser scanning) and structural behaviour simulation tools. The study has two aims, on the one hand, to develop an algorithm able to interpret automatically point clouds acquired by laser scanning systems of beams subjected to different load situations on experimental tests; and on the other hand, to minimize differences between deformation values given by simulation tools and those measured by laser scanning. In this way we will proceed to identify elastic parameters and boundary conditions of structural element so that surface stresses can be estimated more easily.