Ground Module

The GroundClassification module contains a class GP_class which implements an algorithm for ground plane classification in LiDAR point cloud data.

To import the module

import terravide.src.GroundClassification as GP

Module Dependencies

The GroundClassification module has the following dependencies:

• numpy

These modules must be installed before using the functions in this module.

API

GP Class

class GroundClassification.GP_class

The GP class provides methods for ground plane classification using LiDAR point cloud data. The constructor takes one argument, ground_H_thresh_perc, which is the height threshold of the region to look at from the lowest point in the tileset. The default value is 0.1.

The class provides the following method:

Extract_GroundPoints(lidarSubtilePoints)

Extract ground points from a tile points.

Parameters:

lidarSubtilePoints (numpy array) – Numpy array of size Nx3 containing the coordinates of LiDAR points.

Returns:

Two numpy arrays: Ground_Points containing classified ground points and Not_ground_points containing non ground points.

Here is an example of how to use the GP_class class:

import numpy as np
from terravide.src.GroundClassification import GP_class as GP

# Create an instance of the GP_class
gp = GP(ground_H_thresh_perc=0.2)

# Create a numpy array of LiDAR points
lidarSubtilePoints = np.random.rand(100, 3)

# Call the Extract_GroundPoints method to classify ground points
ground_points, not_ground_points = gp.Extract_GroundPoints(lidarSubtilePoints)

# Print the classified ground and non ground points
print("Ground Points: ", ground_points)
print("Not Ground Points: ", not_ground_points)