In this informative video below, Martin Flood, Vice President of Special Projects at GeoCue, provides a demonstration of doing a ground classification in LP360 software with LIDAR data collected with a TrueView 535 3D imaging sensor.
Ground Classification with LiDAR Data
In the demonstration, recorded live from the floor at Geo Week 2023, Martin Flood explains how LP360 can process LiDAR data to create a point cloud, which can be classified using different algorithms to identify the objects in the data set. Ground classification is a prime product generated from LIDAR data, especially when conducting topo work and generating one or two-foot contours. The standard unclassified data set used for this demonstration was collected over a test range at the GeoCue office in Huntsville, Alabama.
Flood points out different objects in the data set, such as trees, vegetation, the parking lot, buildings, and cars parked around. The goal is to extract and identify returns from the ground and build the ground surface without the buildings, vegetation, and other objects that have been classified. LP360 uses an adaptive TIN approach for ground classification, which starts from the lowest surface in the point cloud and merges or molds it to find the lowest continuous surface in the points. Flood mentions that there are user parameters to tweak, which he explains during the actual running of the point cloud task.
Thinning the Data
In the demonstration, Flood also discusses data density, which is a challenge when working with LIDAR data. For instance, Trueview 535 data from drone LIDAR systems is very dense with millions of points collected from the ground and trees. This is often more than needed to generate a good ground surface, especially when generating one or two-foot contours. Flood explains that users can thin the data to reduce data density, making it more manageable for downstream workflows.
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