Towards practical artificial intelligence in Earth sciences
Abstract Although Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research.
Authors
- Annie Burgess (ORCID: https://orcid.org/0000-0002-6026-0336)
- Nicoleta Cristea (ORCID: https://orcid.org/0000-0002-9091-0280)
- Armin Mehrabian (ORCID: https://orcid.org/0000-0003-0587-9387)
- Ziheng Sun (ORCID: https://orcid.org/0000-0001-9810-0023)
- Andrew Huang
- Yuhan Rao (ORCID: https://orcid.org/0000-0001-6850-3403)
- Brianna R. Pagán (ORCID: https://orcid.org/0000-0001-8028-0310)
- Bing Gong (ORCID: https://orcid.org/0000-0001-7770-2738)
- Şebnem Düzgün (ORCID: https://orcid.org/0000-0001-7013-9241)
- Aaron Margolis
- Wendy Hawley Carande (ORCID: https://orcid.org/0000-0003-4424-1458)
- Thilanka Munasinghe (ORCID: https://orcid.org/0000-0002-0911-750X)
- Zhong Liu (ORCID: https://orcid.org/0000-0001-8150-7556)
- Qian Huang (ORCID: https://orcid.org/0000-0002-5502-1023)
- Corin Jorgenson (ORCID: https://orcid.org/0000-0002-0088-6062)
- Talya ten Brink (ORCID: https://orcid.org/0000-0002-3279-0575)
- P. W. Webley (ORCID: https://orcid.org/0000-0001-5327-8151)
- Gerbrand Koren (ORCID: https://orcid.org/0000-0002-2275-0713)
- Bhargavi Janga
- Gokul Prathin Asamani
- Sanjana Achan
- Mike Mahoney
- L. Sandoval
Institutions
- NOAA National Marine Fisheries Service (US)
- University of Geneva (CH)
- East Tennessee State University (US)
- Environmental Protection Agency (US)
- Goddard Space Flight Center (US)
- North Carolina State University (US)
- University of Alaska Fairbanks (US)
- SUNY College of Environmental Science and Forestry (US)
- George Mason University (US)
- Rensselaer Polytechnic Institute (US)
- Colorado School of Mines (US)
- University of Colorado Boulder (US)
- Utrecht University (NL)
- University of Washington (US)
- Montana State University (US)
- Anaconda (United States) (US)
- Laboratory for Atmospheric and Space Physics (US)
- Seattle University (US)
Publication Details
- Journal
- Computational Geosciences
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1007/s10596-024-10317-7
- Citations
- 6
- Primary Topic
- Reservoir Engineering and Simulation Methods
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- National Science Foundation
- National Aeronautics and Space Administration
- Nuclear Safety and Security Commission
- National Oceanic and Atmospheric Administration