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CLOUDCOM
2010
Springer

Evaluation of MapReduce for Gridding LIDAR Data

8 years 9 months ago
Evaluation of MapReduce for Gridding LIDAR Data
-- 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 sharednothing 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.
Sriram Krishnan, Chaitanya K. Baru, Christopher J.
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where CLOUDCOM
Authors Sriram Krishnan, Chaitanya K. Baru, Christopher J. Crosby
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