Methods comparison for LiDAR data reduction in the generation of digital elevation models
DOI:
https://doi.org/10.59192/mapping.434Keywords:
Digital Elevation Models (DEM), LiDAR, Uniform Algorithms, OptD, RpA, PpCAbstract
Digital Elevation Models (DEM) are currently considered as a fundamental tool for the study of the earth's surface, allowing to make engineering and scientific evaluations easier. Among the different methods for obtaining input data, LiDAR (Light Amplification by Stimulated Emission of Radiation) technology provides a more efficient and cost-effective capture process, obtaining a point cloud that allows the construction of higher resolution and high quality DEMs. However, the increase in the density and volume of the point cloud is a factor that makes data processing difficult and can generate errors in the DEM. Currently, there are different methods that are used for the reduction of LiDAR data. The main objective of this study is to compare the most relevant LiDAR data reduction methods and the new proposals, in order to establish which of them has greater technical feasibility. According to the results obtained, it was determined that the methods with the best performance in LiDAR data reduction are the uniform algorithms, the RpA algorithm and the new proposals such as the OptD method and the PpC method.
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