Automatic Powerline Classification

Automatic Powerline Classification is found in the Classification section of the Automatic Point Cloud Analysis tool.

The Powerline Classification option in the Automatic Point Cloud Analysis tool can automatically detect power lines in high density lidar data. This density is typical of terrestrial lidar, mobile lidar, or uav collected lidar point clouds. Photogrammetric point clouds, such as those generated using Pixels to Points, may also have this density, but they do not typically have the reconstruction detail to precisely identify powerlines. There are two methods for powerline classification in Global Mapper Pro:

• Grid is the traditional method and was designed for aerial, fixed wing collected lidar. It classifies points based on their geometric relationship to a best fit plane. This is the method best for aerial lidar with multiple returns in forested areas.
• Max Likelihood uses segmentation, and is developed to work with more modern point clouds such as terrestrial lidar, programmatic, and drone-mounted. The Segmentation Method segments the point cloud into clusters of points, and then determines if those clusters are likely powerline points.

You can choose to run multiple classifications at once by checking multiple options (noise, ground, etc). They will run in a pre-specified order based on method.

Use the settings in the top of the Automatic Point Cloud Analysis window to choose which layers are to be operated on, filter or limit processing, change custom segmentation settings for Max Likelihood, and more.

Once your settings have been determined for all desired classifications, click Classify Features to begin processing.

Classification and Extraction Shared Settings:

Power Line classification shares two settings with the Feature Extraction tool:

Minimum Powerline Length

Sections of powerline that are shorter than this set length will be ignored. Increase this value when using low density point clouds.

Minimum and Maximum Height Above Ground options

Use these fields to specify the minimum and maximum height above ground that a point has to be in order to consider it as a possible powerline point.

Powerline Classification Methods:

The point cloud should have ground points already classified prior to performing this classification. Global Mapper will do this automatically when the ground classification option is also selected to run simultaneously.

The Grid Method

The powerline classification algorithm for the Grid method compares segments of data (bins) to best fit lines, to classify points as powerline that are within a minimum distance from a best-fit line. It recommended to run this data on point clouds with at least 20 points /m².

Maximum Distance from Best Fit Line

The algorithm works by creating a "best fit" line where it estimates the power line to be, then compares the points to the line. This setting determines the maximum distance (in meters) away from the best-fit 3D line of points with similar elevations in a bin, that any points can be and still be considered powerlines.

Bin Size to Check for Linear Points

This setting controls the size of each bin (meters per edge) when evaluating points to see if they follow a linear pattern. Overwrites shared resolution setting.

Maximum Slope (Height Change per Meter)

Use this field to specify the maximum difference in elevation allowed per meter to consider points as possibly part of the same powerline segment. Basically, it measures the amount of slack in the powerline. The default value is 0.5m, which allows for a change in elevation of 0.5m over a 1m distance between points. You might wish to specify a slightly larger value if your data is noisy.

The Max Likelihood Method

The classification of powerline points involves a statistical analysis (Principal Component Analysis) of clusters of nearby points to determine what is likely part of a powerline feature. Points are gathered into clusters based on a measure of similarity between nearby points, with the intent to group points that belong to the same object or feature. Segments are classified by evaluating the likelihood that they have a statistical distribution matching the powerline fingerprint. Segments with linearity less than what is expected from a base powerline fingerprint are rejected.

• 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.

Once your settings have been determined for all desired classifications, click Classify Features to begin processing.

Additional Options

Enable Report

Enable this option to save a text summary of the classification settings and results to the Reports folder, and export a JSON settings file for future reuse or sharing.
To load the saved settings .json file: Open the automated Point Cloud Analysis tool, check the box to Enable Custom Feature Models, then from the list of options that appear, choose Load Models.

Report information

• Class Changes: How many points of each classification category were reassigned a different classification.

• Input : Input point cloud layers and how many points were processed.

• Classifier Setup: Classification settings as entered into the Automatic Point Cloud Analysis dialog.

• Report Artifacts: File path to the saved report and json model.

The following report items pertain to segmentation-based (Max-Likelihood) classification methods, and may or may not appear, depending on the results of the classification:

• Processing: Models run and any tiling of the processing

• Classification Health: Provides information about the results. For example, "Healthy" is a successful classification, versus "Critical" can indicate no points were assigned and suggestions for improving results. "Notable" can indicate that segments best matched a class that was not enabled in this run.

• Unclassified reasons: When a segment doesn't get a class, the report explains why:

  • LowConf (Low Confidence): The segment best matched that class, but its probability score fell below the classifier's required confidence threshold (e.g., segments looked like Medium Vegetation, but were not clear enough to officially classify).

  • NotAssign (Not Assigned / Recipe Disabled): The segment matched a class, but that class was not enabled in your current run settings (e.g., segments matched Pole, but you only ran the Building classification recipe).

  • Other: The segment was excluded due to geometric, height, or spatial filtering criteria (such as falling outside specified area or footprint limits).

• Per-class confidence: Shows the distribution of the classifier's top prediction confidence scores across each class, grouped into 10% percentage brackets (from <10% up to >=90%). Here, p1 refers to the highest probability score calculated for a segment's best-matching class.

• Preferred class: Indicates which class the remaining unclassified segments are likely to be. Even though these segments failed to earn an official label (due to low confidence scores, disabled recipes, or geometric rules), the algorithm tracks which class was their top candidate.

• Assigned with low confidence: Identifies point segments that were successfully assigned to a class, but where the classifier's top choice barely beat out the second-best candidate.

• Columnar drop-reason breakdown: Summary explaining why potential vertical, column-like objects (such as utility poles) were rejected by the classifier rather than saved as confirmed features.

Reset Existing Pole Points to Unclassified at Start

Resets the classified pole point data to unclassified at the beginning of processing.