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.
Highlights
The construction and updating of 3D spatial databases for urban areas by an airborne laser scanner (ALS) has grown in popularity [1,2]
To test the applicability of the method, a raster digital surface model (DSM) was generated from raw ALS point data by interpolating with inverse distance weighting (IDW), and a raster digital terrain model (DTM) was produced from the DSM by local minimum filtering
This paper proposed a parallel processing method for DSM and DTM generation using a PC cluster and a virtual grid, as a methodology of efficient processing of huge amounts of ALS data
Summary
The construction and updating of 3D spatial databases for urban areas by an airborne laser scanner (ALS) has grown in popularity [1,2]. Because traditional algorithms might not run effectively in a parallel environment, their modification to a parallel structure is first necessary if parallel processing is to be most effectively utilized Another problem is that point searches of particular locations cannot be completed in a constant time if the scanned points are not arranged on a proper data structure, because, unlike raster images, they are irregularly distributed geometrically. This paper proposes, as a new framework for the efficient processing of enormous amounts of ALS data, a parallel processing method using a PC cluster and a virtual grid. To test the applicability of the method, a raster DSM was generated from raw ALS point data by interpolating with inverse distance weighting (IDW), and a raster DTM was produced from the DSM by local minimum filtering. Some standards for assessing parallel processing algorithms were adopted for the purpose of evaluating the computational performance of the proposed algorithm
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