ALS technology��s direct, swift and accurate surveying of ground with enhanced point density makes it ideal for DSM and DTM generation. However, the sharply increased, up-to-terabyte-level data quantities that result, represent a serious data processing problem. As data sizes and the complexity of analyzing methods in GIS and remote sensing have grown, parallel processing has been highlighted as a solution [5�C8]. Parallel processing, though a potential ALS-data-processing solution, has not been actively employed in the field. Furthermore, 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. Thus, the specification of an appropriate data structure and a proper data processing methodology are both necessary if the intended efficiency in processing enormous amounts of ALS data is to be realized.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.
A methodology of dealing with boundary data and of selecting interpolation centers in the parallel processing was designed to ensure the same result from the sequential processing. In the present study, results of sequential processing were compared with those of parallel processing. Some standards for assessing parallel processing algorithms were adopted for the purpose of evaluating the computational performance of the proposed algorithm.2.?Background2.1. ALS Data Structure and Virtual GridALS data consists of points distributed AV-951 irregularly in 3D space. These points are stored in the order in which they are scanned, forming a unique trajectory according to the specific type of scanner [9]. However, this pattern can easily become irregular when the laser beam emitted by the scanner meets objects of sharply differing heights or the data undergoes processes such as merging, filtering, or segmentation. Much of ALS data processing relies on the operations of querying points at specific locations along with their neighbors. However, such operations cannot be efficiently executed when ALS point data are stored in common data structures such as the stack or queue [10].
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