A raw point cloud is run through a series of processing steps to label each point with a class, e.g. "Ground", "Low/Medium/High Vegetation", "Building", "Transmission Tower", etc.
There will be a different algorithm for each feature class. For example, points that are part of a building might be identified by finding groups of points that form a very flat surface. ML models can also do this based on training data.
The final digital elevation model (DEM) is then just taking the "Ground" class from the classified point cloud and using them to triangulate a surface. This differs from a digital surface model (DSM), which will triangulate a surface based on ground+building+vegetation points.
https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-...
There will be a different algorithm for each feature class. For example, points that are part of a building might be identified by finding groups of points that form a very flat surface. ML models can also do this based on training data.
https://pro.arcgis.com/en/pro-app/latest/tool-reference/3d-a...
The final digital elevation model (DEM) is then just taking the "Ground" class from the classified point cloud and using them to triangulate a surface. This differs from a digital surface model (DSM), which will triangulate a surface based on ground+building+vegetation points.