EASE-DGGS: a hybrid discrete global grid system for Earth sciences

Jeffery A. Thompson, Mary J. Brodzik, Kevin A.T. Silverstein, Mason A. Hurley, Nathan L. Carlson

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Although we live in an era of unprecedented quantities and access to data, deriving actionable information from raw data is a hard problem. Earth observation systems (EOS) have experienced rapid growth and uptake in recent decades, and the rate at which we obtain remotely sensed images is increasing. While significant effort and attention has been devoted to designing systems that deliver analytics ready imagery faster, less attention has been devoted to developing analytical frameworks that enable EOS to be seamlessly integrated with other data for quantitative analysis. Discrete global grid systems (DGGS) have been proposed as one potential solution that addresses the challenge of geospatial data integration and interoperability. Here, we propose the systematic extension of EASE-Grid in order to provide DGGS-like characteristics for EOS data sets. We describe the extensions as well as present implementation as an application programming interface (API), which forms part of the University of Minnesota’s GEMS (Genetic x Environment x Management x Socioeconomic) Informatics Center’s API portfolio.

Original languageEnglish (US)
Pages (from-to)340-357
Number of pages18
JournalBig Earth Data
Issue number3
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s). Published by Taylor & Francis Group and Science Press on behalf of the International Society for Digital Earth, supported by the International Research Center of Big Data for Sustainable Development Goals, and CASEarth Strategic Priority Research Programme.


  • DGGS
  • Discrete global grid systems
  • EASE Grid
  • coordinate reference systems


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