The Auto-Classify Ground Points tool on the Lidar Toolbar brings up the Automatic Classification of Ground Points settings window (below). These values may be changed to optimize the algorithm output 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.
The ground classification is a two-part process that removes points that are likely not ground, and then compares the remaining points to a modeled 3D curved surface inside a series of bins (i.e. a gridding of the lidar into smaller areas). 
If more than one Lidar data set is loaded into workspace, specific Lidar layers may be selected, or unselected, for classification. Check the option to 'Only Classify Lidar Points Selected in Digitizer Tool' to run classification tool only on points selected by the Digitizer.
Base Bin Size to Check for Curvature Deviations - The primary ground classification algorithm depends on comparing points to their neighbors inside of a small local area. The Base Bin size defines the size of that area. This values may be set in Meters or calculated from the average point spacing of the loaded data.
Smaller distances will result in higher accuracy, but will require higher resolution Lidar data and longer processing time. Those working with low resolution data should use a higher value. A value that captures at least 3 Lidar points inside of each bin is recommended, with larger bin sizes increasing the speed of processing, but also decreasing the accuracy. The default value is 2 meters, or 3 point spacings if the data is low density (less than 1.5 points per square meter).
Measurement in Point Spacings is calculated from the loaded point data. The calculated average point spacing can be viewed in the layer metadata statistics. For reproduce-able results across different data sets, such as continuous tiles of Lidar, specify a fixed value in meters for the Base Bin Size.
Example
The first image simulates a 2 meter bin size on low resolution aerial Lidar.
With this resolution data, a much larger bin size would not contain enough points for good classification results.
The second image simulates a 2 meter bin size on a higher resolution point cloud.
This bin size will process relatively quickly on this point cloud, but could be increased to 1 meter to help with removing low vegetation or including steeper slopes in the ground classification.
Note that many of the higher points in this case will already be removed by the setting described below in Removal of Likely non-ground points before the curvature deviation is checked.
Minimum Height Departure from Local Mean for Non-Ground Point - Specify the allowed height change from the local averaged minimum, at which to remove points from the ground classification. This filter is used in the second part of the algorithm, for modeling a curved ground surface.
The default value of 0.3 meters works well with most medium density data that has a moderate amount of height variance in the observations. Small values will remove low vegetation and brush from the ground classification (in high density data). Larger values (such as 1 meter to 3 meters) are usually necessary for lower resolution lidar data (with a density of 2 points per square meter or less).
The below parameter settings allow the user to control the values used for the removal of points that are not likely to be ground points. This step happens first to remove from consideration points that are not likely to be ground.
Specify the maximum change in elevation for the ground. This should be set to the expected range in elevation of the ground throughout the point cloud.
This first filter helps to remove points that definitely shouldn't be considered ground, and therefore can also speed up the processing of the remaining points. If the expected change in ground height is less than the height of buildings, this can greatly improve classification by removing relatively flat built environment from consideration.
Example
In this data-set the urban area is relatively flat. A Maximum Height Delta of 50 meters, shown in the first image, doesn't successfully remove all of the larger building roofs. Changing the Maximum Height delta to 5 meters, shown in the second image, easily eliminates many of the building structures from the ground class.
Specify the expected slope of the terrain in degrees. Typically built environment, such as building roofs and building walls have steep slopes. Regions of the point cloud that follow continuous surfaces but are steeper than the expected terrain slope will be removed from consideration as ground points. The default value of 7.5° is applicable for relatively flat areas without large embankments.
Example
In the example below the default expected terrain slope of 7.5° is unable to identify the steep ground section on the left side of the profile:
Increasing the Expected Terrain Slope to 20° captures more of the ground points:
To remove large building roofs from the ground classification, specify the maximum building width. Larger values will cause longer processing times, but will help to remove large commercial and industrial building roofs from the ground classification.
This is part of the first filter, and compares the local minimum to neighbors in progressively larger areas. This helps to remove building roofs and other built environment from the ground category, since there are no ground returns inside of the area. Since the filter progressively builds to the maximum building width value, beware that larger values will increase processing time.
Customization of this parameter became available in Global Mapper v19, in previous versions this value was fixed at 50 meters, which is a good value for many urban areas, but may miss eliminating some of the larger building roofs from the ground class.
Example
In the first example a Max Building width of 50 meters is unable to recognize that the large building roof is not ground. Setting the Max building width to 100 meters removes the building roof from the ground class.
Reset Existing Ground Points to Unclassified
at Start - Resets the Unclassified Ground Point data, resetting
any points classified as ground. Removes all manual and automatic classification
of ground points in selected point data.
Specify Bounds... Manually specify
the bounds for applying auto-classification of ground points. May use
North, West, South, East coordinates to define coordinates, or draw an
area feature to define classification boundaries.
Filter Points...Contains additional
settings for filtering by points for classification by elevation and color
values, Source ID and existing classification. The user may also exclude
all points outside of a specified scan angle.
Restore Defaults - Restores the default settings for the Auto-Classify Ground Points tool.