Faculty Publications


Big 3D Spatial data processing using cloud computing environment

Document Type



Amazon Web Services, Cloud Computing, LiDAR, Multi-core CPU

Journal/Book/Conference Title

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2012



First Page


Last Page



Lately, acquiring a large quantity of three-dimensional (3-D) spatial data particularly topographic information has become commonplace with the advent of new technology such as laser scanner or light detection and ranging (LiDAR) and techniques. Though both in the USA and around the globe, the pace of massive 3-D spatial data collection is accelerating, the provision of affordable technology for dealing with issues such as processing, management, archival, dissemination, and analysis of the huge data volumes has lagged behind. Single computers and generic high-end computing are not sufficient to process this massive data and researches started to explore other computing environments. Recently cloud computing environment showed very promising solutions due to availability and affordability. The main goal of this paper is to develop a web-based LiDAR data processing framework called "Cloud Computing-based LiDAR Processing System (CLiPS)" to process massive LiDAR data using cloud computing environment. The CLiPS framework implementation was done using ESRI's ArcGIS server, Amazon Elastic Compute Cloud (Amazon EC2), and several open source spatial tools. Some of the applications developed in this project include: 1) preprocessing tools for LiDAR data, 2) generation of large area Digital Elevation Model (DEMs) on the cloud environment, and 3) user-driven DEM derived products. We have used three different terrain types, LiDAR tile sizes, and EC2 instant types (large, Xlarge, and double Xlarge) to test for time and cost comparisons. Undulating terrain data took more time than other two terrain types used in this study and overall cost for the entire project was less than $100. Copyright © 2012 ACM.

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DOI of published version