One of the most integral parts of LiDAR analysis is determining which points represent the ground. Once this step is completed, we can construct bare earth models (BEMs) and normalize our point clouds to produce reliable estimates of height, canopy height models and other products.
pyfor offers a few avenues for normalization, ground filtering and the creation of bare earth models. All of these methods are covered in the advanced Ground Filtering document. Here, only the convenience wrapper is covered.
The convenience wrapper Cloud.normalize is a function that filters the cloud object for ground points, creates a bare earth model, and uses this bare earth model to normalize the object in place. That is, it conducts the entire normalization process from the raw data and does not leverage existing classified ground points. See the advanced Ground Filtering document to use existing classified ground points.
It uses the Zhang2003 filter by default and takes as its first argument the resolution of the bare earth model:
import pyfor tile = pyfor.cloud.Cloud('my_tile.las') tile.normalize(1)