Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

TL;DR

This paper reviews existing geospatial data handling theories and methods in the context of big data challenges, highlighting current limitations and proposing future research directions for improved management and analysis.

Contribution

It critically assesses the capability of traditional geospatial data handling methods to manage emerging big data challenges and suggests areas for further development.

Findings

Current methods face scalability issues with big data

Traditional theories need adaptation for diverse data formats

Identifies key challenges and future research directions

Abstract

Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future. Keywords: Big data, Geospatial, Data handling, Analytics, Spatial Modeling, Review