摘要：This article explores the use of an advanced, high-memory cloud-computing environment to process large-scale topographic light detection and ranging (LiDAR) data. The new processing techniques presented herein for LiDAR point clouds intelligently filter and triangulate a data set to produce an accurate digital elevation model. Ample amounts of random-access memory (RAM) allow the employment of efficient hashing techniques for spatial data management; such techniques were utilized to reduce data distribution overhead and local search time during data reduction. Triangulation of the reduced, distributed data set was performed using a local streaming approach to optimize processor utilization. Computational experiments used Amazon Web Services Elastic Compute Cloud resources. Analysis was performed to determine (1) the accuracy of the binning/array-based reduction, as measured by root mean square error and (2) the scalability of the approach on varying-size clusters of high-memory instances (nodes having 244 GB of RAM). For experimental data sets, topographic LiDAR data generated by the Iowa LiDAR Mapping Project was used. This article concludes that the data-reduction strategy is computationally efficient and outperforms a comparable randomized filter control when moderate reduction is undertaken – e.g., when the data set is being reduced by between 30% and 70%. Performance speed-up ratios of up to 3.4, comparing between a single machine and a 9-node cluster, are exhibited. A task-specific stratification of the results of this work demonstrates Amdahl’s law and suggests the evaluation of distributed databases for geospatial data.
