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

arXiv: https://arxiv.org/abs/1511.03010 · HTML: https://mlsanity.com/paper/1511.03010/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2015.

## Built on by (most cited first, 9 of 9)

- [GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis](https://mlsanity.com/paper/2604.13888/) (2026)
- [Designing Decision Support Systems for Emergency Response: Challenges and Opportunities](https://mlsanity.com/paper/2202.11268/) (2022)
- [Towards Intelligent Geospatial Data Discovery: a knowledge graph-driven multi-agent framework powered by large language models](https://mlsanity.com/paper/2603.20670/) (2026)
- [On the semantics of big Earth observation data for land classification](https://mlsanity.com/paper/2204.11082/) (2022)
- [Multi-dimension Geospatial feature learning for urban region function recognition](https://mlsanity.com/paper/2207.08461/) (2022)
- [GeoNDC: A Queryable Neural Data Cube for Planetary-Scale Earth Observation](https://mlsanity.com/paper/2603.25037/) (2026)
- [Automated Quality Assessment of Geospatial Vector Data: A GeoAI Approach using Spatial Representation Learning](https://mlsanity.com/paper/2606.28390/) (2026)
- [Shape-Based Inductive Bias for Glioma Grading from Tumor Contours](https://mlsanity.com/paper/2607.26090/) (2026)
- [PlacePulse: Learning Transferable Place Representations from Structural Geographic Data](https://mlsanity.com/paper/2610.13390/) (2026)

## Abstract (arXiv)

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