Global Mapper Pro is a state-of-the-art lidar processing software. This software has the power to process point clouds through automated machine learning-based methods while also providing a variety of powerful manual tools. These tools are capable of handling just about any type of point cloud data, regardless of collection method. Global Mapper Pro can extract a variety of meaningful information from a point cloud, from automated classification and feature extraction to specific metrics such as slope, vegetation encroachment, powerline slack, tree density, and more. 

With the constant growth in the demand for 3D point clouds, the ability to collect high-resolution data at speed via UAV can increase efficiency in workflow across all industries that use spatial data. Pairing Global Mapper Pro’s processing power with data collected from one of LiDARUSA’s new scanners is a clear path to successful data capture and processing. 

LiDARUSA

LiDARUSA, a leading builder of economical UAV and mobile mapping systems that often works with Blue Marble Geographics, has integrated a new scanner into its UAV offerings. This scanner, as described, allows for better ground and vegetation classification. Global Mapper Pro’s Point Cloud tools handle new data from cutting-edge scanners such as this with ease.

Manually deriving information from data sets is an arduous task that can be simplified by applying a classification. Differentiation of points through classifications adds structural meaning to the data. For years, Global Mapper has provided an array of built-in automatic classification tools to identify bare ground, vegetation, building, pole, and wire points. This toolset has been made infinitely more dynamic with the addition of segmentation and custom point cloud classification. 

This section will show how Global Mapper Pro’s point classification toolset is easily able to handle this unique UAV-collected lidar data. 

Automatic Classification Tools: Built-In Options for Standard Features 

The Automatic Point Cloud Classification tools can be used to easily identify target features within the point cloud. These built in options cover the most commonly classified features including bare ground, buildings, various levels of vegetation, powerlines and poles. 

Different classification methods are available to fit different point cloud structures and feature types. Global Mapper Pro has a classification method designed specifically for high-resolution point clouds collected via drone, terrestrial scanner, and more. The Max likelihood method, a machine learning method, was designed for high-resolution and terrestrial point clouds. Max Likelihood is a segmentation-based method. For each classification type, the tool has been tailored to find clusters of points with the common shapes and characteristics of these features in the point cloud.

Global Mapper Pro provides options for segmentation and custom classification methods to identify other, less standard objects in the point cloud. 

Segmentation: Isolate Point Clusters Based on Attribute and Shape Patterns

Segmentation is a lidar classification method that clusters points together into groups, or “clusters,” based on common attributes. Its goal is to group together “things that are things”; for example, a car is a thing, a tree is a thing, the paint stripes on a road, the sidewalk, or a specific species of grass. Just about anything that has a different “fingerprint” of attributes from its neighbors can be classified using the Segmentation tool. In the above image, the segmentation tool separated clusters of points that represent individual trees. This is similar to how the Max Likelihood method in automatic classification works for vegetation identification, except without using preset variables. Segmentation is also the unofficial first step in training a custom classification tool. 

For more information on segmentation, see: 

• Point Cloud Segmentation Analysis in Global Mapper Pro
• Classifying Transmission Towers in Point Clouds using Global Mapper Pro

Custom Classification Through Machine Learning

When it comes to surveys of structures and niche objects for industry-specific scenarios, automatic classification tools may not be sufficient. In such cases, machine learning techniques can be applied to customize automatic unique features that are not part of the standard feature set. Global Mapper Pro’s award-winning Custom Classification Training tool enables the creation of custom classifications that can automatically identify target features within a point cloud.

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Before

Swipe to see how this point cloud was segmented to cluster road points, which were then classified (purple) with the custom classification tool. 

How does Custom Classification Work?

Custom classification uses the same machine learning segmentation-based analysis as built-in Max Likelihood classifications to assess point cloud characteristics and find commonalities among the points that make up an object. For example, the road points classified in the above point cloud could be distinguished by their neighbors based on intensity values and curvature. This method operates on the assumption that each object in the point cloud, each cluster of points identified with segmentation analysis, has a signature made up of attributes and/or structures that differentiate it from its neighbors. The Custom Point Cloud Classification tool takes advantage of these signatures to classify specific and unique features in a point cloud. When training the classification, selecting points by segment will help to ensure that all points have similar signatures. Once created, this custom classification can be applied to other point clouds.

Interested in learning more about custom classification? See: 

• How to Train a Custom Point Cloud Classification in Global Mapper Pro
• Using Machine Learning to Classify Unique Features in Point Cloud Data: Bridge Infrastructure

Manual Classification: Fine-Tuning and Cleaning Lidar Data 

Have a few points you’d like to clean up? Among the plethora of classification methods in Global Mapper Pro, the manual classification options provide easy and dynamic methods for manually editing point clouds.

Point classifications can be updated manually by selecting the required points and applying a preconfigured or custom classification. With the Path Profile tool and the 3D viewer, it’s easy to parse through complex structures in a point cloud and select just the points that need editing. In particular, the Path Profile Viewer allows you to see and edit the data from perpendicular and parallel perspectives. These data slices provide the necessary finesse for creating high-accuracy classification assignments. 

Application Example: Locating Areas of Powerline Vegetation Encroachment 

Corridor inspections can be performed on the ground or by flying above the corridor to inspect the right-of-way for vegetation encroachments. Imagine being able to survey your powerlines at 30mph, then be able to automatically highlight areas of encroachment. Global Mapper provides an easy-to-use solution for identifying possible vegetation encroachment before plant life contacts utilities. Using lidar data, with its ability to penetrate vegetation, it is possible to identify potential hazards before going into the field for a ground inspection. 

Extracting Vector Features: Powerlines

One workflow that is commonly accomplished using high-resolution data collected via drone/UAV is powerline mapping. After the powerline points have been classified using the Automatic Classification Tool, they can be extracted as vector line features. These line features inherit the attribute information from the point cloud, such as elevation.

Identifying Areas of Vegetation Encroachment

With the ability to collect data at high speeds, as LiDARUSA has displayed with this dataset, identifying these encroachments for inspection has gotten easier. 

After extracting the power lines, the potential right-of-way encroachments are highlighted using the Select Lidar by Distance tool. As a result, the vegetation points that fall within a certain distance of the extracted powerline are selected. In the example below, we chose a Maximum Search Distance of 2m to search on either side of the power lines. This tool works on terrain layers as well as vector layers, and can search for any type of lidar points. Here, the lidar was Filtered to just vegetation because we are primarily concerned with tree encroachment.

These areas of encroachment can also be viewed in Global Mapper Mobile, a simplified version of the desktop application for iOS and Android devices, providing easy access for maintenance crews or other “boots on the ground” employees. For each cluster of points, a vector bounding box can be created to demarcate the location and size of each encroachment. Once exported with any other desired data, each crew can update the workspace from a handheld iOS or Android device to mark completed areas, and send the data back to the office to monitor the progress of the project.  

Additional Popular Lidar Processes in Global Mapper Pro

Are you interested in other common workflows involving drone-collected data? Check out these resources: 

• Top 5 Pixels to Points Features to Improve Your Photogrammetric Processing Workflow
• Improving the Quality of Lidar Data in Global Mapper
• Creating a Digital Terrain Model in Global Mapper Pro
• Create Canopy Height Models from Lidar in Global Mapper Pro
• Easily Created Slope Maps in Global Mapper

WORK MADE EASY WITH GLOBAL MAPPER

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