A multi-source spatio-temporal data cube for large-scale geospatial analysis

Abstract

Data management and analysis are challenging with big Earth observation (EO) data. Expanding upon the rising promises of data cubes for analysis-ready big EO data, we propose a new geospatial infrastructure layered over a data cube to facilitate big EO data management and analysis. Compared to previous work on data cubes, the proposed infrastructure, GeoCube, extends the capacity of data cubes to multi-source big vector and raster data. GeoCube is developed in terms of three major efforts: formalize cube dimensions for multi-source geospatial data, process geospatial data query along these dimensions, and organize cube data for high-performance geoprocessing. This strategy improves EO data cube management and keeps connections with the business intelligence cube, which provides supplementary information for EO data cube processing. The paper highlights the major efforts and key research contributions to online analytical processing for dimension formalization, distributed cube objects for tiles, and artificial intelligence enabled prediction of computational intensity for data cube processing. Case studies with data from Landsat, Gaofen, and OpenStreetMap demonstrate the capabilities and applicability of the proposed infrastructure.

Keywords:

• Data cube
• high-performance computing
• earth observation
• cloud computing
• artificial intelligence

Acknowledgements

We appreciate Professor Jennifer Miller and three reviewers for their constructive comments that helped improve the quality of the paper. The work was supported by National Natural Science Foundation of China (No. 42090011 and No. 42071354), and Major Science and Technology Projects of Hubei Province (No. 2020AAA004).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available in https://doi.org/10.6084/m9.figshare.15032847.

Additional information

Funding

The work was supported by National Natural Science Foundation of China [No. 42090011 and No. 42071354], and Key Research and Development Program of Hubei, China [No. 2020AAA004].

Notes on contributors

Fan Gao

Fan Gao is a Ph.D. student in the School of Remote Sensing and Information Engineering at Wuhan University. His research interest is in high-performance geoprocessing and GIS. He contributed to the idea, study design, methodology, implementation, and manuscript writing of this paper.

Peng Yue

Peng Yue is a professor at Wuhan University. He serves as the director at Hubei Province Engineering Center for Intelligent Geoprocessing and the director at the Institute of Geospatial Information and Location Based Services. He supervised the research, and contributed to the idea, study design, methodology, and manuscript writing of this paper.

Zhipeng Cao

Zhipeng Cao is a Ph.D. student in the School of Remote Sensing and Information Engineering at Wuhan University. His research interest is in big spatio-temporal data stores. He contributed to the idea, methodology, and implementation.

Shuaifeng Zhao

Shuaifeng Zhao is a Ph.D. student in the School of Remote Sensing and Information Engineering at Wuhan University. His research interest is in big spatio-temporal data stores and processing. He contributed to the idea, methodology, and implementation.

Boyi Shangguan

Boyi Shangguan is a Ph.D. student in the School of Remote Sensing and Information Engineering at Wuhan University. His research interest is in high-performance geoprocessing and GIService. He contributed to the idea, methodology, and implementation.

Liangcun Jiang

Liangcun Jiang is an associate research fellow in the School of Remote Sensing and Information Engineering at Wuhan University. His research interests include Earth observation data infrastructure, data provenance, and geospatial semantic web. He contributed to the idea, study design, and methodology.

Lei Hu

Lei Hu is a postdoctoral researcher in the School of Remote Sensing and Information Engineering at Wuhan University. His research interests include Earth observation science and systems, geospatial interoperability and standards, and environmental modelling in agriculture and disaster management. He contributed to the idea, study design, and methodology.

Zhe Fang

Zhe Fang is a Ph.D. student in the School of Remote Sensing and Information Engineering at Wuhan University. His research interest is in disaster emergency response. He contributed to the idea, methodology, and implementation.

Zheheng Liang

Zheheng Liang is the chief engineer of South Digital Technology Co., Ltd. He currently is the special reviewer of the journal ‘Geographic Information World’, and an expert in the comprehensive evaluation expert database of Guangdong Province. He contributed to the idea, study design, and methodology.