Classifying Lidar with the push of a (few) button(s)!
If you are working with any type of point cloud data, the Global Mapper Lidar Module is a powerful, must-have add-on to the desktop application. One of the standout features of the Module is its ability to automatically identify and apply the appropriate ASPRS classification to each point with a few clicks. This blog will walk through the steps required to automatically classify a point cloud.
Global Mapper’s Lidar auto-classification tools provide the means to identify ground, buildings, utility lines and poles, vegetation, and noise points within an unclassified point cloud. Each of the classification processes requires the presence of ground points in the point cloud so this is a good place to start. If necessary, noise classification can be used to automatically identify any points that are beyond the expected elevation range when compared to those in close proximity. This cleanup tool is used to remove obvious anomalies in the data. At this stage, buildings and trees can be classified and if the point cloud is of sufficient density, there are even tools to classify above-ground utility lines and poles.
When you begin the auto-classification process and load your point cloud into the software, it is important to know that Global Mapper has the ability to display points in several different ways including by RGB value (if present), intensity, and classification. For this process, we will color the Lidar by classification. If your point cloud has never been classified, it will look similar to this:
After the data is loaded, you are ready to classify ground points. To do this, locate the Auto-Classify Ground Points button in the toolbar. This tool brings up the Automatic Classification of Ground Points settings window. These values will need to be adjusted based on the local terrain, the range of elevation values in the data set, user-defined preferences for filtering points prior to auto-classification, or known features in the landscape. This will help to optimize the output. When you have applied the necessary settings, click the OK button to initiate the process.
If necessary, your next step will be to click the Auto-Classify Noise Points button. Identifying previously unclassified noise points will clean up the point cloud and improve further classification results.
At this stage, the non-ground points or points representing buildings and vegetation, are ready to be identified and classified. In the Lidar module, buildings and vegetation are classified using the same algorithm, and the dialog box can be accessed using the Auto-classify Buildings and Vegetation button. The parameters required in the classification process describe the expected structure of buildings and trees within the point cloud. These values can be adjusted to account for the characteristics of your specific point cloud.
The Auto-Classify Powerline and Pole points button can automatically detect above-ground cables, and/or pole-like objects, such as utility poles, in high-density Lidar data with at least 20 points /m2. This density is typical of terrestrial Lidar and mobile Lidar point clouds. While synthetic Lidar (photogenerated Lidar) may also have this density, it does not typically have the reconstruction detail to precisely identify power lines or pole-like objects. Similar to the other classification tools, this process looks for structures resembling powerlines or poles based on user settings.
After you have classified your point cloud, you can begin analyzing the data further. This may involve creating a terrain model or extracting vector features from the classified point cloud.
Keep an eye out for our upcoming blog, focused on the lidar QC process!
To learn more about the Lidar Module’s automatic classification tools please check out the Global Mapper Knowledge Base and if you have any further questions about the auto-classify tools please contact email@example.com.