OpenTopography: a services oriented architecture for community access to LIDAR topography
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
GPU-based cloud performance for LiDAR data processing
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
GPU-based roofs' solar potential estimation using LiDAR data
Computers & Geosciences
Big 3D spatial data processing using cloud computing environment
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
High-resolution spatial interpolation on cloud platforms
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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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.