Abstract

This paper focuses on a topic barely considered in the literature: how to improve the accuracy of a given Digital Elevation Model (DEM) irrespective of its lineage pointing out to its most suspicious values (also denoted here as outliers). Certainly, there exist methods tailored to a specific procedure and source (contour maps, remote sensing image, etc.), but they are not valid for other cases. This is a delicate problem for both the producer and end user. Here we reported the results of a comparison of two methods using six DEMs intended to be representative of different landscapes. Both methods have been applied to each DEM, producing a prescribed number of height candidates to be analyzed. Assuming that all candidates are wrong, their elevations have been blindly replaced by interpolated heights, simulating the behavior of the inexperienced user. The so improved (or degraded) DEM is compared against the ground truth, and updated accuracy figures are calculated. The experiment shows that the RMSE diminishes an amount between roughly 2 and 8 per cent of the original value by changing less than 1 per cent of the elevations in the dataset.

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