ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 55-62, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-55-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
10 Jul 2015
BUILDING SPATIOTEMPORAL CLOUD PLATFORM FOR SUPPORTING GIS APPLICATION
W. W. Song1, B. X. Jin2, S. H. Li2,3, X. Y. Wei2,4, D. Li2, and F. Hu5 1Department of Geoinformation Science, Kunming University of Science and Technology, 68 Wenchang Road, Kunming, Yunnan, China
2Yunnan Provincial Geomatics Centre, 404 West Ring Road, Kunming, Yunnan, China
3College of Tourism & Geographic Sciences, Yunnan Normal University,768 Juxian Street in Chenggong District, Kunming, Yunnan, China
4College of Geographic Sciences, Nanjing Normal University,No.1,Wenyuan Road,Xianlin University District,Nanjing,China
5Center for Intelligent Spatial Computing, George Mason University, 4400 University Dr., Fairfax, VA, USA
Keywords: Spatiotemporal Cloud Platform, HDFS, MapReduce, GIS Application, Geospatial Analysis Abstract. Traditional geospatial information platforms are built, managed and maintained by the geoinformation agencies. They integrate various geospatial data (such as DLG, DOM, DEM, gazetteers, and thematic data) to provide data analysis services for supporting government decision making. In the era of big data, it is challenging to address the data- and computing- intensive issues by traditional platforms. In this research, we propose to build a spatiotemporal cloud platform, which uses HDFS for managing image data, and MapReduce-based computing service and workflow for high performance geospatial analysis, as well as optimizing auto-scaling algorithms for Web client users’ quick access and visualization. Finally, we demonstrate the feasibility by several GIS application cases.
Conference paper (PDF, 1113 KB)


Citation: Song, W. W., Jin, B. X., Li, S. H., Wei, X. Y., Li, D., and Hu, F.: BUILDING SPATIOTEMPORAL CLOUD PLATFORM FOR SUPPORTING GIS APPLICATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 55-62, https://doi.org/10.5194/isprsannals-II-4-W2-55-2015, 2015.

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