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I'm taking point cloud xyz's from LIDAR and turning them into DEMs, then comparing that DEM, to a DEM of the original 3D CAD Models to see how well the LIDAR is measuring the model for elevation and slope. I'm wondering of any methods to analyse the data in comparison of one another to highlight the inaccuracies of the LIDAR?

I'm thinking visually through the DEM's & Slope map. And also using the raw data, trying to find the difference in Z at corresponding X,Y points? I'm quite new to LIDAR data though, I imagine its going to be messy.

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You do not mention what software you are using so here is a basic path to calculating the root mean square error between your interpolated LiDAR derived surface and the surveyed quality data of your CAD file.

I have an old ArcMap 10.1 python script to do the RMSE calculations and I will post that to this answer if you request it. I have not tried these tools but here is a link to an ArcGIS Pro tool to accomplish the steps below. Here is an ArcMap tool link.. And here is a QGIS tutorial.

  1. Generate about 35 (or more) random points that fall inside both of your datasets.
  2. Create a column in those points and populate it with the z values of the CAD data. ArcGIS has an Add surface information tool in the 3D analyst toolbox, and QGIS has an add surface raster surface value tool (although I do not think those tool work with CAD files).
  3. Create a column in those points and populate it with the z values of the LiDAR derived surface.
  4. Calculate the RMSE values.

The lower the RMSE the closer the two surface are in values at those sample points. Experiment with different spatial resolution outputs with your LiDAR derived DEMs until you get the closest fit surface to the survey data.

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