A significant portion of the data used for different purposes includes location information. As a result, there is an increasing effort to find a general framework by utilizing crucial technological drivers. Similarly, as geospatial data becomes more accessible; the possibilities for changing scientific discoveries and methods in modern society are endless. Naturally, many applications depend on the accurate and efficient geospatial data processing. However, using the traditional method to process and compute geospatial data frequently presents several difficulties.
The explanation for this is the large volume, heterogeneity, and distributed nature of these data. Consequently, it is imperative to implement contemporary techniques that can manage the computational and analytical demands of this massive amount of geospatial data. Many of these systems—particularly those related to High-Performance Computing (HPC) technology—have been described in the literature. Therefore, the goal of this article is to review the application of HPC technology to big data processing in geospatial applications. The review’s findings show that predictive data science tools like parallel and grid computing are available in geographic big data applications, enabling quick and effective processing that will help create a sustainable ecosystem. Also, the efficient management of large data sets requires storage, visualization, analytics, and analysis. Of course, this review demonstrated how recent advancements in computing have impacted geospatial data handling
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