Geospatial Big Data for Environmental and Agricultural Applications

  • Athanasios Karmas
  • Angelos Tzotsos
  • Konstantinos Karantzalos


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.


Cloud Computing Geospatial Data Raster Data Normalize Difference Water Index Hadoop Distribute File System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Athanasios Karmas
    • 1
  • Angelos Tzotsos
    • 1
  • Konstantinos Karantzalos
    • 1
  1. 1.Remote Sensing LaboratoryNational Technical University of AthensAthensGreece

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