Application of Data Reduction Methods in Dynamic TIN Models to Topographic LIDAR Data
نویسندگان
چکیده
Comparisons of five data reduction methods associated with dynamic TIN models were conducted. All methods were applied to real world Light Detection and Ranging (LIDAR) topographic data. Data reduction is based on point selection by thresholding in dynamic Delaunay triangulation together with random point selection. The triangulation criteria used include Delaunay and hybrids of Delaunay and data dependent triangulation. The performance of the various reduction methods was evaluated by means of surface area, volume, RMS of vertical errors and maximum vertical errors. All methods were evaluated for five levels of reduction; 10%, 5%, 2.5%, 1% and 0.5% of full datasets.
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