Point Cloud Sub-Classifications in Global Mapper Pro

The ability to create custom point cloud classifications opens the door to another level of point cloud management: sub-classifications. Creating Custom Feature Classifications in a point cloud is a cutting-edge new tool originally released in version 25 of Global Mapper Pro. This new functionality allows users to leverage machine learning techniques to train a custom model. This model looks for specific features in a point cloud for classification. Assigned classifications can be any built-in classes such as ground or building, or a new class such as sidewalk or fire hydrant. What happens though, when a class needs to be further refined, or have other classes identified within it? That is where point cloud sub-classifications become beneficial.

What is a Sub-Classification? 

Point cloud sub-classifications, released earlier this year in Global Mapper Pro v25.1, allow users to refine an existing class. Sub-classes are part of an already existing class. Once trained as a custom feature classification, the sub-classes are defined with their own classification code, name, and color – uniquely identifying the feature of interest.

Sub-classifications can be applied to a variety of use cases, from identifying cart paths on a golf course, to particular roof structures in buildings. In the below example, we’ll take a look at an airport and attempt to identify runway paint markings on the runway, which is already classified as ground.

Creating a Sub-Classification

When creating a custom classification, whether an initial class or a sub-class, the process focuses on using attributes of the training samples to develop what’s called a signature. These attributes are similar to what the segmentation tool uses to identify clusters of points: physical structure, return number, etc. Once created, this signature is used to identify subsequent points which fit that criteria. The point cloud below has RGB values, which will be a key attribute in distinguishing the paint from the runway. This process can also rely on segmentation outputs to help build signatures. For more information, see: How to Train a Custom Point Cloud Classification in Global Mapper Pro.

The first step in the process was to run the built-in automatic classifiers. This allowed for the identification of ground and vegetation, along with buildings and powerlines (not pictured). You’ll notice that the ground classification encompasses runway, grassy areas, and other similar flat, ground-level features.

In many scenarios, the above classification is exactly what is intended. Ground is classified regardless if that ground is paved, grassy, or otherwise. In situations where more detail is required, sub-classification is the next step.

A sub-classification is set up nearly identically to a custom-trained classification. The major difference being the “Is subclass of” option, which in this case will be set to ground. Train the classification by selecting points that represent the feature to be classified. In this example, when collecting training samples, displaying the data by RGB allowed for clearer visualization of the paint markings. Segmenting the point cloud ahead of time can help with creating clusters of points for selection.

Once the training is complete, the classifier can be run again, this time focusing on the newly created runway markings sub-class which was created previously. The result of which is the independent classifications of the paint markings. The white color was randomly assigned and can be customized by the user as needed.

Tip: Classification color can be edited from the Lidar section of the Configuration menu.

Once assigned a classification, these sub-class points can be distinguished from the parent class without being excluded from it. For example, if we were to create a digital terrain model using only ground points, because the paint points are a subclass of ground, they would be included in the model creation as well. Sub-classifications give you more control over your data for fine-tuning and more accurately analyzing the output. 

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