Title: Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges Authors (with equal contribution): Songnian Li *, Ryerson University, Toronto, Canada,
[email protected] Suzana Dragicevic, Simon Fraser University, Vancouver, Canada,
[email protected] François Anton, Technical University of Denmark, Lyngby, Denmark,
[email protected] Monika Sester, Leibniz University Hannover, Germany,
[email protected] Stephan Winter, University of Melbourne, Australia,
[email protected] Arzu Coltekin, University of Zurich, Switzerland,
[email protected] Chris Pettit, University of Melbourne, Australia,
[email protected] Bin Jiang, University of Gävle, Sweden,
[email protected] James Haworth, University College London, UK,
[email protected] Alfred Stein, University of Twente, The Netherlands,
[email protected] Tao Cheng, University College London, UK,
[email protected] * 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.