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Big 3D spatial data processing using cloud computing environment

Published: 06 November 2012 Publication History

Abstract

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.

References

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Hodgson, M. E.; Jenson, J. R.; Schmidt, L.; Schill, S.; Davis, B. 2003. An evaluation of lidar- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs. Remote Sens. Environ. 84, 295--308.
[2]
Sugumaran, R., Oryspayev, D., and Gray, P. 2011. GPU-based cloud performance for LiDAR data processing. COM.Geo 2011: 2nd International Conference and Exhibition on Computing for Geospatial Research and Applications, May 23--25, 2011, Wahington DC, USA.
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Oryspayev, D., Sugumaran, R., DeGroote, J., and Gray, P. 2011. LiDAR data reduction using vertex decimation and processing with GPGPU and multi-core CPU technology. Computers and GeoSciences (Accepted).
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Han, S. H., Heo, J., Sohn, H. G., and Yu, K. 2009. Parallel Processing Method for Airborne Laser Scanning Data Using a PC Cluster and a Virtual Grid. Sensors 2009, 9 (4): 2555--2573;
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Krishnan, S., Bary, C., Crosby, C., 2010. Evaluation of MapReduce for Gridding LIDAR Data, CloudCom, pp. 33--40, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.
[6]
Iowa Lidar Mapping Project (ILMP), GeoInformatics Training, Research, Education, and Extension (GeoTRE) Center of University of Northern Iowa, http://geotree2.geog.uni.edu/lidar/ Accessed on August 30, 2012.
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LAStools. http://www.cs.unc.edu/~isenburg/lastools/ Accessed on February 11, 2011.

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cover image ACM Conferences
BigSpatial '12: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
November 2012
116 pages
ISBN:9781450316927
DOI:10.1145/2447481
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 November 2012

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Author Tags

  1. Amazon web services
  2. LiDAR
  3. case studies of data intensive computing in the clouds
  4. cloud computing
  5. multi-core CPU

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  • Amazon Company

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Overall Acceptance Rate 32 of 58 submissions, 55%

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Cited By

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  • (2024)Data reduction in big data: a survey of methods, challenges and future directionsInternational Journal of Data Science and Analytics10.1007/s41060-024-00603-zOnline publication date: 10-Jul-2024
  • (2021)Geospatial Edge-Fog Computing: A Systematic Review, Taxonomy, and Future DirectionsMobile Edge Computing10.1007/978-3-030-69893-5_3(47-69)Online publication date: 27-Feb-2021
  • (2019)The case for dual-access file systems over object storageProceedings of the 11th USENIX Conference on Hot Topics in Storage and File Systems10.5555/3357062.3357080(13-13)Online publication date: 8-Jul-2019
  • (2019)AgniProceedings of the ACM Symposium on Cloud Computing10.1145/3357223.3362703(390-402)Online publication date: 20-Nov-2019
  • (2017)RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing EnvironmentsJournal of Sensor and Actuator Networks10.3390/jsan60300176:3(17)Online publication date: 15-Aug-2017
  • (2016)Big Data Reduction Methods: A SurveyData Science and Engineering10.1007/s41019-016-0022-01:4(265-284)Online publication date: 10-Dec-2016
  • (2013)On the link(s) between “D” and “A” in Mobile Data Analytics2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2013.6547442(144-151)Online publication date: Apr-2013

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