Some LP360 Point Cloud Tasks (PCT’s) were originally designed for older manned lidar datasets, which have significantly lower point density than modern sUAS datasets. TrueView and other sUAS sensors have the capability of producing much denser point clouds, so these processes can take much longer if running on the entire dataset. A common strategy and workflow for processing high density datasets is to thin the point cloud first before running some tasks on the dataset, such as the Building Filter and Extractor. This involves running a few PCT’s in sequence to greatly improve processing speed. This workflow is briefly reviewed below in sequential order:
Classify by Statistics
Run the Classify by Statistics PCT to thin the point cloud to a roughly 8 points per square meter level.
Run the Building Filter (Planar Point Filter PCT) on the thinned dataset. This initial classification may need some cleanup/manual classification before proceeding to the next step.
Run the Building Extractor (Point Tracing and Squaring PCT) on the building classification of the thinned dataset.
Run the Proximity Classifier PCT to incorporate the classification of the thinned class subset back into the original dataset.