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1

School of Civil and Environmental Engineering, Yonsei University - 134 Sinchon-dong Seodaemun-gu, Seoul 120-749, Korea

2

Department of Civil, Urban and GeoSystem Engineering, Seoul National University - 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea





*

Author to whom correspondence should be addressed.



Abstract In this study, a parallel processing method using a PC cluster and a virtual grid is proposed for the fast processing of enormous amounts of airborne laser scanning ALS data. The method creates a raster digital surface model DSM by interpolating point data with inverse distance weighting IDW, and produces a digital terrain model DTM by local minimum filtering of the DSM. To make a consistent comparison of performance between sequential and parallel processing approaches, the means of dealing with boundary data and of selecting interpolation centers were controlled for each processing node in parallel approach. To test the speedup, efficiency and linearity of the proposed algorithm, actual ALS data up to 134 million points were processed with a PC cluster consisting of one master node and eight slave nodes. The results showed that parallel processing provides better performance when the computational overhead, the number of processors, and the data size become large. It was verified that the proposed algorithm is a linear time operation and that the products obtained by parallel processing are identical to those produced by sequential processing. View Full-Text

Keywords: ALS; LiDAR; Parallel processing; Virtual grid; PC cluster; DSM; DTM ALS; LiDAR; Parallel processing; Virtual grid; PC cluster; DSM; DTM





Autor: Soo Hee Han 1, Joon Heo 1,* , Hong Gyoo Sohn 1 and Kiyun Yu 2

Fuente: http://mdpi.com/



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