Poisson disk sampling in geodesic metric for DEM simplification

Abstract To generate highly compressed digital elevation models (DEMs) with fine details, the method of Poisson disk sampling in geodesic metric is proposed. The main idea is to uniformly pick points from DEM nodes in geodesic metric, resulting in terrain-adaptive samples in Euclidean metric. This method randomly selects point from mesh nodes and then judges whether this point can be accepted in accordance with the related geodesic distances from the sampled points. The whole process is repeated until no more points can be selected. To further adjust the sampling ratios in different areas, weighted geodesic distance, which is in relation to terrain characteristics, are introduced. In addition to adaptability, sample distributions are well visualised. This method is simple and easy to implement. Cases are provided to illustrate the feasibility and superiority of the proposed method.

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