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Semantic Earth Observation Data Cubes

Author

Listed:
  • Hannah Augustin

    (Interfaculty Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria)

  • Martin Sudmanns

    (Interfaculty Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria)

  • Dirk Tiede

    (Interfaculty Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria)

  • Stefan Lang

    (Interfaculty Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria)

  • Andrea Baraldi

    (Italian Space Agency (ASI), 00133 Rome, Italy)

Abstract

There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data.

Suggested Citation

  • Hannah Augustin & Martin Sudmanns & Dirk Tiede & Stefan Lang & Andrea Baraldi, 2019. "Semantic Earth Observation Data Cubes," Data, MDPI, vol. 4(3), pages 1-19, July.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:3:p:102-:d:249105
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    References listed on IDEAS

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    1. Michael K. Buckland, 1991. "Information as thing," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 42(5), pages 351-360, June.
    2. Catharina Riedemann & Hardy Pundt & Francis Harvey & Werner Kuhn & Yaser Bishr, 1999. "Semantic interoperability: A central issue for sharing geographic information," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 33(2), pages 213-232.
    3. Michael Buckland, 1991. "Rejoinder to “Information as thing”," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 42(10), pages 758-758, December.
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    Cited by:

    1. Gregory Giuliani & Gilberto Camara & Brian Killough & Stuart Minchin, 2019. "Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes," Data, MDPI, vol. 4(4), pages 1-6, November.
    2. Gregory Giuliani & Elvire Egger & Julie Italiano & Charlotte Poussin & Jean-Philippe Richard & Bruno Chatenoux, 2020. "Essential Variables for Environmental Monitoring: What Are the Possible Contributions of Earth Observation Data Cubes?," Data, MDPI, vol. 5(4), pages 1-25, October.

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