A review of Earth Artificial Intelligence

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dc.contributor.authorSun, Ziheng
dc.contributor.authorSandoval, Laura
dc.contributor.authorCrystal-Ornelas, Robert
dc.contributor.authorMousavi, S. Mostafa
dc.contributor.authorWang, Jianwu
dc.contributor.authorPurushotham, Sanjay
dc.contributor.authoret al
dc.date.accessioned2022-09-26T14:57:16Z
dc.date.available2022-09-26T14:57:16Z
dc.date.issued2022-01-11
dc.descriptionAuthors: Ziheng Sun, Laura Sandoval, Robert Crystal-Ornelas, S. Mostafa Mousavi, Jinbo Wang, Cindy Lin, Nicoleta Cristea, Daniel Tong, Wendy Hawley Carande, Xiaogang Ma, Yuhan Rao, James A. Bednar, Amanda Tan, Jianwu Wang, Sanjay Purushotham, Thomas E. Gill, Julien Chastang, Daniel Howard, Benjamin Holt, Chandana Gangodagamage, Peisheng Zhao, Pablo Rivas, Zachary Chester, Javier Orduz, Aji Johnen_US
dc.description.abstractIn recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to “blow away the fog to get a clearer vision” about the future development of Earth AI. The paper covers all the majorspheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and prewarnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future.en_US
dc.description.sponsorshipThis work is sponsored by NASA ACCESS (#80NSSC21M0028 and #80NSSC21M0027, 2020), DOE (DE-AC02-05CH11231), NSF EPSCoR (#2019609, 2020), NSF Geoinformatics program (EAR-1947893 & EAR1947875, 2020), NASA Health and Air Quality project (#80NSSC21K0512, 2018), NASA PO program (#80NM0018D0004), NSF Cybertraining (#2117834, 2021), NSF CSSI (#1835717, 2018), NSF EarthCube (#2126315, 2021). This paper has gone through NASA internal review and been cleared for publication.en_US
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S0098300422000036en_US
dc.format.extent16 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2jhku-xd7x
dc.identifier.citationSun, Ziheng et al. "A review of Earth Artificial Intelligence." Computers & Geosciences, 159 (11 January 2022). https://doi.org/10.1016/j.cageo.2022.105034.en_US
dc.identifier.urihttps://doi.org/10.1016/j.cageo.2022.105034
dc.identifier.urihttp://hdl.handle.net/11603/25879
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.subjectUMBC Big Data Analytics Laben_US
dc.titleA review of Earth Artificial Intelligenceen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170en_US

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