Building Filter and Extraction for dense datasets

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.

Building Filter

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.

Building Extractor

Run the Building Extractor (Point Tracing and Squaring PCT) on the building classification of the thinned dataset. See the Point Group Tracing and Squaring Point Cloud Task User Guide for more detailed information. If you have a number of buildings, use Feature Analyst to QA/QC the building extraction results.

Proximity Classifier

Run the Proximity Classifier PCT to incorporate the classification of the thinned class subset back into the original dataset.