New Approaches for Estimating the Local Point Density and its Impact on Lidar Data Segmentation

This article describes how Light Detection and Ranging (LIDAR) systems have been created for the rapid collection of high density three-dimensional (3D) point cloud data over the past few years. The advent of these systems has reduced the cost and increased the availability of accurate 3D data for diverse applications such as terrain mapping, transportation planning, emergency response, 3D city modeling, heritage documentation, forest parameter estimation, flood hazard mapping, and coastal management. Usually, the original LIDAR point cloud does not comprise semantic information about the type and characteristics of reflecting surfaces. Therefore, the data that has been collected should be processed to extract useful information for the applications mentioned above, such as ground and non-ground classification, Digital Terrain Model (DTM) generation, and building hypothesis generation.