Classifying Power Poles with the New Segmentation Option in Global Mapper

The Automatic Point Cloud Analysis tool Global Mapper Pro includes the ability to classify Power Poles using a segmentation method called “Max Likelihood”. This tool works by identifying cylindrical patterns in the point cloud, similar to how flat planes are used to identify walls in buildings. Features in the point cloud are identified based on their shape and attributes. This blog describes how the tool works under the hood, and provides settings recommendations. You don’t need to know how segmentation works to use the tool, but it’s interesting!

How segmentation is used to find poles

Segmentation (Max Likelihood) is a classification method that is built, and better suited, for how modern lidar is collected. Originally lidar was collected with a sparse aerial sensor, and that is what the older tools like the gridded option were built for. Modern lidar tends to be very dense and is often collected by sensors mounted on things other than planes, such as terrestrial data from tripods or cars or aerially from drones.

Technically, if you’re interested in doing this the hard way, power poles can also be classified in the manual Geometric Segmentation tool. This lets you tweak individual settings. Cylinders/poles have a specific curvature value, among other attributes. The pole in the first image worked well with a Minimum Cluster value of 10 and a Standard Deviation of 3. The benefit of using the Max Likelihood method instead is that the settings are already tailored to look for pole structures. It also includes the ability to filter by pole diameter and automatically filter by height to weed out other cylindrical objects that are not poles. More information on how segmentation works, in general, can be found here.

Settings Tips

Choose the Max Likelyhood setting for Pole Classification, then scroll up to return to the Classification and Extraction Shared Settings. These settings will be applied to both pole classification, and pole extraction should you shoes to use that tool later on. 

The minimum and maximum settings are used to help differentiate poles from trees. Cylinder features that are outside of these set values will not be classified. Use the measure tool to measure your pole in the data to acquire this value. 

Trees are often measured at the same height above ground, called diameter at breast height (DBH). Power poles, functionally unlike trees, are typically uniform in diameter, so poles are assumed to have a constant diameter over the pole and aren’t measured at a certain height above ground. You do still want to classify ground points first because height above ground is used when measuring pole height to verify that it is indeed a pole and something shorter such as a bollard.

Keep the point spacing (neighborhood range) large enough that the poles are still visible. For example, if you are only looking at a 0.1- meter patch, a telephone pole will look pretty flat instead of the cylinder we are looking to classify. 

Keep the Resolution large enough that the poles are still visible. For example, if you are only looking at a 0.1 meter patch, a telephone pole will look pretty flat instead of the cylinder we are looking to classify. Sometimes the neighborhood range (and stuff) that works best for classifying poles is different from what works for buildings and other classification tools. This, of course, changes based on your data as every point cloud is different based on how and what was collected. These settings, as you may remember from the Troubleshooting Lidar blog series, are how you tell Global Mapper what to expect with your specific data. 

Learn more about exciting new functionality in Global Mapper Pro by downloading a free 14-day trial today and testing the program on your own!