Tips for Powerline and Pole Classification
Welcome to part three of the Tips for Troubleshooting Point Cloud Classification series, focusing on helping users better understand Global Mapper’s Automatic Classification tools and how they can be adjusted for variations in point clouds. In the first blog, we covered Noise and Ground Classification, then the Non-Ground Classification Tool, used for Building and Vegetation classification. Now, we will cover Powerline and Pole classification.
First, as we work through the classification tools, be sure you have already classified noise, ground, and vegetation points. Remember that the classification tools are loosely made to be run in a specific order: noise, ground, buildings and vegetation, then powerline and powerpole. The order is important because it helps narrow the focus of the classification and weed out other points.
When mapping power lines and similar structures, be sure that the point cloud is dense enough to have captured the powerlines. As power lines are thin structures, sparse point clouds often don’t contain enough powerline points to capture the structure. The minimum recommended resolution is at least 20 points /m2.
This option has been superseded by the segmentation method available in version 24.1.
The Powerline Classification tool identifies linear features above ground, such as power lines or cables. As with all automatic classification tools, it’s often easiest to run the default settings, then look at your data to see what hasn’t been captured and adjust the settings to meet those needs. For larger point clouds, you can use the Specify Bounds option to troubleshoot settings on a smaller portion of the data to save processing time.
The powerline classification tool works by comparing sections (bins) of points against a hypothetical best fit line. Points that are within the set distance of the best fit line are classified as powerlines. We, humans, can easily identify line features in the data. This tool identifies those features by using statistics to measure the nearness of points.
Visual description of the powerline settings from the Global Mapper Knowledge Base.
The Maximum Height Change can be thought of as how much sag is in the line. You can use the Path Profile Tool to measure the height change along the line. The path profile tool is also a great way to manually reclassify any straggling points that were omitted from the automatic classification.
Before classifying poles, classify the powerline points first, so they are omitted from the pole classification. Pre-existing powerline and vegetation classification can help distinguish poles from trees. Many poles were once trees, after all.
If points that should be part of the powerline aren’t being classified, you can increase the Maximum Distance from Best Fit Line. This sets a threshold around a recognized linear pattern of points. If Global Mapper identifies a line of points, this threshold is how far to look from the line to find other linear points.
If vegetation is being classified as a powerline, try classifying the vegetation first. The Powerline classification looks for points in a line, and branches often fit that description. You can also use the Filter Lidar tool to turn off the vegetation points, omitting them from classification without removing them from the dataset.
Swipe to see how increasing both the Bin Size and Maximum Height Change from default to “1” improved the classification of this terrestrial lidar.
*Note: The gridded pole classification method described below has been superseded by a new segmentation method located in the non-ground classification tool in Global Mapper v24.1. More information on that option can be found in this blog.
The pole classification option is in beta as of v24.0. This tool can identify cylindrical pole structures, as shown in the screenshot below. If you would like to classify transmission towers or pylons, use the Segmentation tool.
The first few settings in the menu, Minimum Height of Pole, Points per pole, and Classification threshold are to help distinguish poles from similar structures that aren’t poles. The Classification Threshold is an estimate of how closely the pole feature resembles a straight up and down stack of points. This specifically helps to distinguish poles from wider structures like signposts or trees.
This tool isn’t built to identify transformers and support structures, but with some data sets, the Maximum Horizontal Extent can be adjusted to capture those as well. Similar to the powerlines, the Maximum Distance from the Best Fit Line setting is a width threshold from the cylindrical structure.
Increasing the Maximum Horizontal Extent can better capture additional pole-mounted structures.
These settings adjustments should get you started and enable you to get better results from the automatic classification of poles and powerlines. Stay tuned for one more blog covering the other classification tool: segmentation.
Contact firstname.lastname@example.org for more specific help in classifying your data. More information about Global Mapper lidar classification tools and settings can be found in the Knowledge Base.
For full lessons on how to manage your data in Global Mapper, consider enrolling in an online, hands-on training class.
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