Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

Research output: Contribution to journalJournal article – Annual report year: 2015Researchpeer-review

  • Author: Li, Songnian

    Ryerson University, Canada

  • Author: Dragicevic, Suzana

    Simon Fraser University, Canada

  • Author: Anton, François

    Geodesy, National Space Institute, Technical University of Denmark, Elektrovej, 2800, Kgs. Lyngby, Denmark

  • Author: Sester, Monika

    Leibniz Universität Hannover, Germany

  • Author: Winter, Stephan

    University of Melbourne, Australia

  • Author: Coltekin, Arzu

    University of Zurich, Switzerland

  • Author: Pettit, Chris

    University of New South Wales, Australia

  • Author: Jiang, Bin

    University of Gävle, Sweden

  • Author: Haworth, James

    University College London, United Kingdom

  • Author: Stein, Alfred

    University of Twente, Netherlands

  • Author: Cheng, Tao

    University College London, United Kingdom

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Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.
Original languageEnglish
JournalI S P R S Journal of Photogrammetry and Remote Sensing
Volume115
Pages (from-to)119-133
ISSN0924-2716
DOIs
Publication statusPublished - 2016
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Big data, Geospatial, Data handling, Analytics, Spatial Modeling, Review

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