2013 IEEE 5th International Conference on Cloud Computing Technology and Science (2010)
Indianapolis, Indiana USA
Nov. 30, 2010 to Dec. 3, 2010
The MapReduce programming model, introduced by Google, has become popular over the past few years as a mechanism for processing large amounts of data, using shared-nothing parallelism. In this paper, we investigate the use of MapReduce technology for a local gridding algorithm for the generation of Digital Elevation Models (DEM). The local gridding algorithm utilizes the elevation information from LIDAR (Light, Detection, and Ranging) measurements contained within a circular search area to compute the elevation of each grid cell. The method is data parallel, lending itself to implementation using the MapReduce model. Here, we compare our initial C++ implementation of the gridding algorithm to a MapReduce-based implementation, and present observations on the performance (in particular, price/performance) and the implementation complexity. We also discuss the applicability of MapReduce technologies for related applications.
MapReduce, Gridding, Digital Elevation Models, LIDAR
Christopher Crosby, Chaitanya Baru, Sriram Krishnan, "Evaluation of MapReduce for Gridding LIDAR Data", 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, vol. 00, no. , pp. 33-40, 2010, doi:10.1109/CloudCom.2010.34