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Geospatial Big Data for Environmental and Agricultural Applications

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Big Data Concepts, Theories, and Applications

Abstract

Earth observation (EO) and environmental geospatial datasets are growing at an unprecedented rate in size, variety and complexity, thus, creating new challenges and opportunities as far as their access, archiving, processing and analytics are concerned. Currently, huge imaging streams are reaching several petabytes in many satellite archives worldwide. In this chapter, we review the current state-of-the-art in big data frameworks able to access, handle, process, analyse and deliver geospatial data and value-added products. Operational services that feature efficient implementations and different architectures allowing in certain cases the online and near real-time processing and analytics are detailed. Based on the current status, state-of-the-art and emerging challenges, the present study highlights certain issues, insights and future directions towards the efficient exploitation of EO big data for important engineering, environmental and agricultural applications.

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Notes

  1. 1.

    www.esa.int.

  2. 2.

    http://terra.nasa.gov.

  3. 3.

    http://aqua.nasa.gov.

  4. 4.

    www.opengeospatial.org/standards.

  5. 5.

    http://www.copernicus-masters.com/index.php?kat=winners.html&anzeige=winner_t-systems2014.html.

  6. 6.

    https://www.digitalglobe.com.

  7. 7.

    www.earthserver.eu.

  8. 8.

    www.planetserver.eu.

  9. 9.

    http://www.helix-nebula.eu/usecases/esa-ssep-use-case.

  10. 10.

    http://www.copernicus-masters.com/index.php?kat=winners.html&anzeige=winner_bsc2014.html.

  11. 11.

    http://www.globalforestwatch.org/.

  12. 12.

    http://ceph.com/docs/master/rbd/rbd/.

  13. 13.

    http://www.gluster.org/.

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  1. Remote Sensing Laboratory, National Technical University of Athens, Zographou Campus, 15780, Athens, Greece

    Athanasios Karmas, Angelos Tzotsos & Konstantinos Karantzalos

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  1. Athanasios Karmas
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  2. Angelos Tzotsos
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Correspondence to Athanasios Karmas .

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  1. School of Information Technology, Deakin University, Burwood, Victoria, Australia

    Shui Yu

  2. Schl of Comp Science & Engg, The Univ of Aizu, Aizu-Wakamatsu City, Japan

    Song Guo

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Karmas, A., Tzotsos, A., Karantzalos, K. (2016). Geospatial Big Data for Environmental and Agricultural Applications. In: Yu, S., Guo, S. (eds) Big Data Concepts, Theories, and Applications . Springer, Cham. https://doi.org/10.1007/978-3-319-27763-9_10

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