Automated Lidar Analysis Tools

Introduction    

Global Mapper has several tools that allow for automated classification and feature extraction. For each analysis tool, there are default recommended input values, along with the option to customize input values for optimizing either processing speed or accuracy.

As with other editing tools in Global Mapper, the original lidar files will not be edited, but changes to the files will be saved in the workspace or preserved in export of the lidar data once classified. See also SAVE - Save Changes Over Original File Data.

Some of these automated Lidar Analysis tools include:

Lidar Auto-Classification Tools

Lidar Automatic Noise Classification

Lidar Automatic Ground Classification 

Lidar Automatic Non-Ground Classification

Lidar Feature Extraction

Extract Vector Features 

About Lidar Auto-Classification

Global Mapper's lidar auto classification tools allow you to identify and classify noise, ground and non-ground points from unclassified point clouds. When you run auto classifications on multiple input files, the same classification parameters are applied to each input file. If ground conditions and quality of lidar points vary by file, then files should be classified separately. The accuracy of auto-classification results will depend on the the level of detail and the quality of point cloud data, in addition to the operator's knowledge of ground/terrain conditions. Post-processing and Quality Assurance steps can be used to optimize classification settings when defaults do not yield satisfactory results. Global Mapper's Path/Profile tool will have the Lidar Toolbar available, so that manual classification of points in a profile view may be conducted.

Outlying elevation values can lead to spurious or inaccurate classification results. Laser pulse returns from obstructions such as haze, air craft, or birds or multiple reflections from tree canopies can result in outlying points with very high elevation values. Ground structures and trees or tree canopies can create multiple reflections, leading to excessively long travel times back to the lidar sensor and creating points with outlying low elevation values. These points with anomalous high and low elevation values are noise points, and can be most easily and quickly seen by coloring or drawing point clouds by elevation. The noise points will expand the expected elevation range for the area. If your lidar data has noise points, and you plan on using the Auto-Classify Ground Points and Auto-Classify Non-Ground Points tools, you will want to Auto-Classify Noise points first. You can also filter out extreme spikes by using the option to 'Delete Samples Over _ Standard Deviations from the Mean' in the Lidar Load Options menu.

Most lidar data will contain a certain percentage of ground points, along with a number that are unclassified. The Auto-Classification Ground Points tool can be used to identify previously unclassified ground points for use in eliminating unclassified points as possible building or tree features, or for employing in the creation of a digital terrain model. Once ground points have been classified a different algorithm may be employed to identify previously unclassified non-ground features, such as buildings, trees, and powerlines by using the Auto-Classify Non-Ground Points. In the classification of non-ground points, relatively flat surfaces that are above the height determined to be ground height will be classified as buildings and those that are vertically offset from neighboring points by user-defined parameters will be classified as trees or powerlines.

The typical workflow for auto-classification of unclassified lidar points is to classify noise points, ground points, and then non-ground points. Identifying previously unclassified noise points will improve the ground auto-classification results, and classifying previously unclassified ground points will improve the auto-classification of non-ground points. Some of the classification dialogs and will allow for user defined bin size settings, the bin size you want to set is based on the resolution of your Lidar data.