Updates to Point Cloud Analysis in Global Mapper Pro v25

Previously, processing a point cloud required repeated steps as you opened each individual tool and specified settings such as bounds and resolution. The new Automatic Point Cloud Analysis dialogue combines these separate tools into one window. Some of these settings are shared not only between classifications but also between classification and extraction. To take it a step further, classifications can now be trained to fit your point cloud better or created from scratch to find unique features in the point cloud using the Custom Classification Training Tool. 

This window consists of four collapsible sections: Input Configuration, Classification and Extraction Settings, Classification, and Advanced Options (including Segmentation). Returning users will be familiar with the majority of these tools and their settings, although they are organized differently. 

The settings found in Input Configuration and Classification and Extraction Settings apply to all or most tools, where applicable.

What Does Classification Look Like in the New Tool?

All of the automatic classification methods from previous versions are now available in one window. Previously, each method had its own tool, with separate settings that all needed to be run sequentially. Now you can check one or all of the classifications, and Global Mapper will run them in a prespecified order depending on the methods chosen. The typical order is noise, ground, building, vegetation, powerline, then pole. Additional settings to further tailor the classifications can be found in the Classification and Extraction Shared Settings section.  

Tip: the Undo functionality also works on classifications. Press (ctrl z) to undo recent actions.

New Automatic Classification Methods 

Returning users may remember the “Non-Ground” Building, Vegetation, and Pole classification tool that offered two different analysis methods for identifying structures in a point cloud: Gridding and Segmentation (now called Max Likelihood). This concept has been expanded to ground and noise classifications. Use the drop-down menus to choose a method for each class:

Gridding, also called MCC (Multiscale Curvature Classification), is the original legacy method. Lidar used to be predominantly gathered by fixed-wing aircraft, and that’s the type of data that this method is built to analyze. Gridding has been largely superseded by the Max Likelihood method. For data collected by means other than nadir, fixed-wing aircraft, use the Max Likelihood method. 

Max Likelihood is a segmentation-based method. For each classification type, the tool has been tailored to find clusters of points that have the common shapes and characteristics of these features in the point cloud. While the Max Likelihood methods have fewer settings than the Grid methods, there are additional options available to adjust the method further:

• The settings in the Geometric Segmentation tool can be used in Max Likelihood classifications. Simply open the Geometric Segmentation tool, choose your settings, and check the box to Use Custom Segmentation Parameters. These settings will influence how the point cloud is segmented.  
• To train the algorithm for your specific feature, check the box to enable Custom Feature Models. These settings will influence how the point cloud is classified. See Custom Classification Training from the below section to learn more.

Classification and Shared Extraction Settings 

Values set in this section tie into all applicable tools, saving you time and sanity. Feature Models apply to all classification methods and feature extractions, and it is a guiding part of Custom Feature Classifications. These are settings that may or may not need adjusting but are available.

Geometric Segmentation 

Segmentation is a powerful tool in Global Mapper. It is a user-guided, automatic tool that breaks the point cloud into smaller groups called segments based on the spatial and attribute relationships between points in the point cloud. It finds objects in a point cloud by looking at the attributes and structure of the points. For example, to segment paint stripes on the road, you would look for points that make up a flat surface, have the same color attribute, etc. By segmenting a point cloud into smaller sections, users are better able to select clusters of points for manual classification or to train a custom classification. 

To see an example use case, check out this blog with the old-style dialog box on using segmentation to classify transmission towers. These segmentation settings are transferable to the new dialog and can be used to train a custom classification tool.

Custom Classification Training Tool

Custom Classification is a new function in Global Mapper. This tool gives users the ability to define custom classifications based on user-created training samples. Before starting the custom classification training process, it’s easiest if you start by segmenting the point cloud. Segmentation operates on the assumption that each “object” in the point cloud, each cluster of points, has a signature made up of attributes and/or structures that separate it from its neighbors. The new Custom Classification Training tool takes advantage of these signatures and lets you train a custom classification tool to look for specific signatures in point clouds. This is especially useful for finding similar objects in multiple point clouds. Instructions for using this tool can be found in the Knowledge Base. Keep an eye out for a new blog describing this process in detail!

Extracting Vector Features 

After a point cloud has been classified, features can be extracted as 3D point, line, or area features using the Feature Extraction tool. This is another place where the shared Feature Model settings come in handy. Settings used to classify features will then be used to extract them. Extracted layers will inherit many of the attributes of the original features, such as height, elevation, or crown canopy width. New in this tool is an improved method for measuring tree canopy areas and all new 3D mesh features to estimate the structure of tree canopies. 

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