Monday, October 16, 2017

Filtering LiDAR data by height above ground

For this tutorial we will be using CloudCompare (version 2.8,

Lidar point clouds normally come with elevations above sea level (or some other datum), which is 
extremely useful. However, we can convert the Z-values into a height above the ground as way to add another layer of data so that we can measure things like average tree heights, examine building heights., or (one thing I do a lot) filter the points to so that we only see points in a specific height range above the ground. This last application is what we'll be covered here and it's really useful to examine river channels below dense vegetation.

  1. In  CloudCompare, load your LAS file. Except the defaults for the popup's that come.
  2. If your point cloud has a lot of extraneous data (outside your area of interest), now is a good time to use the "Cross Section" or "Segment" tools to trim down the points to just the area you need. I usually leave a little around the edges. 
  3. For your segmented point cloud, we'll do a couple of steps to establish the ground surface.
    • Switch the "Active Scalar Field" in the Properties sidebar to Classification

    • Using the "Filter Points By Value" tool, we'll extract the points with a classification values of 2 (the default ground classification) 
      • The Range is 2.0 - 2.0
      • Use the Export option to create a new point cloud
    • Next we will create a mesh surface from the ground points.
      • Select the ground points in the DB Tree sidebar
      • In the "Edit" menu, select Mesh > Delaunay 2.5D (XY Plane)
      • Max edge length can be left at 0
      • Click OK to create the mesh

  4. Next we'll calculate the distance from the ground mesh to the points, which gives us height above the ground.
    • In the DB Tree select both the ground mesh and the segmented point cloud (using Control-Click on Windows to select multiple items)
    • Activate the "Calculate Cloud/Mesh Distance" tool in the toolbar 
    • Leave the defaults and click "Compute" (This will take a little time...)

  5. Now we have a new attribute on our segmented point cloud, available in the "Active Scalar Field" dropdown in the Properties sidebar - "C2M signed distances"
    Colored by Elevation (Above Sea Level)
    Colored by Height Above Ground
    • In the property sidebar, you can adjust the color ramp, and usually you'll need to adjust the bottom saturation value to zero.

  6. There are two ways to filter the data at this point: "Filter Points By Value" or using temporary adjustments.
    • Temporary adjustments can be made in the "SF Display Params" section of the Properties sidebar.
      • First thing to do is click on the Parameters tab and uncheck the "show NaN..." option. This will make points outside the range disappear.
      • Back on the "Display Ranges" tab, you can adjust the displayed range using the circle markers on the graph display. You can also enter specific values in the two text boxes at the top.
      • I usually change the saturation values to match the displayed range too. This ensures that your color ramp is maximized for the displayed data.
    • To extract a specific range, use the "Filter Points By Value" tool like we did above and enter the upper and lower values you wish to extract.
    • As you zoom in and out to navigate, it will be necessary to define the rotation point of the point cloud (otherwise you'll go mad trying to keep things in view).
      • Click on the "Pick rotation Center" tool on the toolbar (Cross hair icon)
      • Click on a point in the middle of the area you want to look at.
      • As you move around you can do this as many times as you need to.
    • Another thing to do is increase the default point size, hover your mouse upper-left of the 3D view to increase or decrease the point size. 
    • One more helpful tool is the "Eye Dome Lighting" filter on the tool bar, this simulates a hillshade-like shading on the points.
All points - height above the ground
Filtered points - 10 feet above the ground


  1. Thank you, just what I was looking for

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