How to Handle the Buildings?

 Lidar point clouds can create amazing DEMs, and are increasingly the source data for DEMs from national mapping agencies.


USGS 1/3 arc second DEM covering Annapolis.  This is a DTM, but roads, bridges, and cloverleafs create many anomalies.  The arrow points out the most egregious, half of a building.  Guth (2018a) pointed out that castles, or indeed any large buildings, create challenges for DTM creation.

Short of lots of editing, are there better alternatives?  Three different DEMs can be created automatically from a lidar point cloud (Guth and others, 2021).


DSM, or digital surface models, selects the highest return in each pixel of the DEM, or the average of the first returns in each pixel.  For a small pixel size, the choice does not make much difference.


NVS, or non-vegetated surface, selects the lowest return in each pixel of the DEM.  If the density of the lidar survey is sufficient, trees and power lines will be removed, leaving the ground and buildings.  In this case it also leaves in a number of vehicles.


DTM, or digital terrain model, uses the points classified as "ground" and takes the lowest point in each pixel or the average of all ground points.  Almost all point clouds have a ground classification done by the contractor, and results are generally satisfactory except for the water and building voids.  Void filling is not a simple operation, and does not create a normal terrain surface, but leaves a series of planar areas.


DTM with holes replaced with the corresponding elevation from the NVS.  In this case it removes the vehicles present in the NVS.

NVS with 0.5 m spacing instead of the 1 m spacing in the maps above.  The lidar point density is not sufficient to get a ground return in every pixel, and the trees leave artefacts


References:

Guth, P.L., 2018a, Castles in the Clouds: LiDAR for Historical Study and Terrain Analysis: Scientia Militaria - South African Journal of Military Studies, Vol 46, No 1, p.79-95, DOI: https://doi.org/10.5787/46-1-1226

Guth PL., 2018b, What should a bare earth digital terrain model (DTM) portray? PeerJ Preprints 6:e27053v1 https://doi.org/10.7287/peerj.preprints.27053v1 .

Guth, P.L.; Van Niekerk, A.; Grohmann, C.H.; Muller, J.-P.; Hawker, L.; Florinsky, I.V.; Gesch, D.; Reuter, H.I.; Herrera-Cruz, V.; Riazanoff, S.; López-Vázquez, C.; Carabajal, C.C.; Albinet, C.; Strobl, P. Digital Elevation Models: Terminology and Definitions. Remote Sens. 2021, 13, 3581. https://doi.org/10.3390/rs13183581

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