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Geospatial Big Data Handling with High Performance Computing: Current Approaches and Future Directions

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High Performance Computing for Geospatial Applications

Part of the book series: Geotechnologies and the Environment ((GEOTECH,volume 23))

Abstract

Geospatial big data plays a major role in the era of big data, as most data today are inherently spatial, collected with ubiquitous location-aware sensors such as mobile apps, the global positioning system (GPS), satellites, environmental observations, and social media. Efficiently collecting, managing, storing, and analyzing geospatial data streams provide unprecedented opportunities for business, science, and engineering. However, handling the “Vs” (volume, variety, velocity, veracity, and value) of big data is a challenging task. This is especially true for geospatial big data since the massive datasets must be analyzed in the context of space and time. High performance computing (HPC) provides an essential solution to geospatial big data challenges. This chapter first summarizes four critical aspects for handling geospatial big data with HPC and then briefly reviews existing HPC-related platforms and tools for geospatial big data processing. Lastly, future research directions in using HPC for geospatial big data handling are discussed.

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Authors and Affiliations

  1. Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA

    Zhenlong Li

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  1. Zhenlong Li
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  1. Center for Applied Geographic Information Science, Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA

    Wenwu Tang

  2. Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Shaowen Wang

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Li, Z. (2020). Geospatial Big Data Handling with High Performance Computing: Current Approaches and Future Directions. In: Tang, W., Wang, S. (eds) High Performance Computing for Geospatial Applications. Geotechnologies and the Environment, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-47998-5_4

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