Faculty Publications

Title

LiDAR data reduction using vertex decimation and processing with GPGPU and multicore CPU technology

Document Type

Article

Keywords

GPU, LiDAR, Multicore CPU, TIN

Journal/Book/Conference Title

Computers and Geosciences

Volume

43

First Page

118

Last Page

125

Abstract

Airborne light detection and ranging (LiDAR) topographic data provide highly accurate representations of the earth's surface. However, large data volumes pose computing issues when disseminating and processing the data. The main goals of this paper are to evaluate a vertex decimation algorithm used to reduce the size of the LiDAR data and to test parallel computation frameworks, particularly multicore CPU and GPU, in processing the data. In this paper we use a vertex decimation technique to reduce the number of vertices available in a triangulated irregular network (TIN) representation of LiDAR data. In order to validate and verify the algorithm, the authors have used last returns only (LRO) and all returns (AR) of points from four tiles of LiDAR data taken from flat and undulating terrains. The results for flat terrain data showed decimation rates of roughly 95% for last returns only and 55% for all returns without significant loss of accuracy in terrain representation. Accordingly, file sizes were reduced by about 96.5% and 60.5%. The processing speed greatly benefited from parallel programming using the multicore CPU framework. The GPU usage demonstrated an additional impediment caused by noncomputational overhead. Nonetheless, tremendous acceleration was demonstrated by the GPU environment in the computational part alone. © 2011 Elsevier Ltd.

Original Publication Date

6-1-2012

DOI of published version

10.1016/j.cageo.2011.09.013

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