Point Cloud Classification in LiDAR360
Raw LiDAR point clouds are comprised of individual points that represent the 3D-spatial locations of the laser scan pulse-reflecting objects present in the survey area at the time of data capture. These coordinate value sets can be attributed with information related to the echo return number (in multiple-return laser scanners), intensity metrics, RGB values from imagery, and classification codes. Classified point clouds allow users of LiDAR to leverage the information type while conducting a wide range of analyses. For example, when points are labeled, or classified, it becomes possible to use them in the study of vegetated coverage, the delineation of building footprints for individual structures, the modeling of energy infrastructures such as powerlines, or the construction of contour maps during the production of topographic survey deliverables.
LiDAR360 provides a comprehensive set of automatic and interactive point cloud classification tools. This tutorial introduces users with a workflow used to automatically classify ground points and vegetation points. It also provides examples that focus on the use of LiDAR360’s interactive classification tools as well as its Machine Learning classification functions. After going through this tutorial, LiDAR360 users should be able to select and combine classification tools to produce workflows that best fit their specific point cloud classification needs.
The Sample Data folder provides sample datasets for the following exercises: