Volume II-4/W2
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.
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

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 W. W. Song et al.
  • 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.