Title: Geospatial Big Data Handling Theory and Methods: a Review and Research Challenges
Title: Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges Authors (with equal contribution): Songnian Li *, Ryerson University, Toronto, Canada, snli@ryerson.ca Suzana Dragicevic, Simon Fraser University, Vancouver, Canada, suzanad@sfu.ca François Anton, Technical University of Denmark, Lyngby, Denmark, fa@space.dtu.dk Monika Sester, Leibniz University Hannover, Germany, monika.sester@ikg.uni-hannover.de Stephan Winter, University of Melbourne, Australia, winter@unimelb.edu.au Arzu Coltekin, University of Zurich, Switzerland, arzu@geo.uzh.ch Chris Pettit, University of Melbourne, Australia, cpettit@unimelb.edu.au Bin Jiang, University of Gävle, Sweden, bin.jiang@hig.se James Haworth, University College London, UK, j.haworth@ucl.ac.uk Alfred Stein, University of Twente, The Netherlands, stein@itc.nl Tao Cheng, University College London, UK, tao.cheng@ucl.ac.uk * Corresponding author 1 Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges Abstract: 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 classified 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, analysing, visualising 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.
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