Torrico-Irahola, Ramiro
University Martí, United Mexican States
DOI : https://doi.org/10.47191/ijmra/v8-i01-26Google Scholar Download Pdf
ABSTRACT:
Digital Elevation Models (DEMs) have become an indispensable tool for a wide range of surface investigations, facilitating critical engineering and scientific assessments. While there are several methods for acquiring input data, LiDAR technology offers a significant advantage by providing a highly efficient and cost-effective means of data acquisition. The technology produces dense point clouds, enabling the construction of high resolution and high quality DEMs. However, the inherent density and large volume of these point clouds present significant data processing challenges, potentially introducing errors into the final DEM. While numerous data reduction methods for LiDAR point clouds have been proposed, previous studies have yielded inconclusive results regarding the optimal method for generating DEMs from LiDAR data, highlighting the need for further research to establish best practices in the field of earth surface analysis. The main objective of the present study is to assess the most relevant LiDAR data reduction methods and new proposals in order to determine which of them are more technically feasible. To this end, a bibliographical study was carried out on the nine selected methods, which allowed their comparison by means of a criteria-based matrix. According to the results obtained, it was determined that the methods with the best performance in LiDAR data reduction are the PpC method, the OptD method, the Uniform 3D Grids and the RpA algorithm.
KEYWORDS:Data Reduction Model, Digital Elevation Models, LiDAR, PpC method, OptD method, RpA algorithm.
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Volume 08 Issue 01 January 2025
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