TY - JOUR
T1 - A review of Earth Artificial Intelligence
AU - Sun, Ziheng
AU - Sandoval, Laura
AU - Crystal-Ornelas, Robert
AU - Mousavi, S. Mostafa
AU - Wang, Jinbo
AU - Lin, Cindy
AU - Cristea, Nicoleta
AU - Tong, Daniel
AU - Carande, Wendy Hawley
AU - Ma, Xiaogang
AU - Rao, Yuhan
AU - Bednar, James A.
AU - Tan, Amanda
AU - Wang, Jianwu
AU - Purushotham, Sanjay
AU - Gill, Thomas E.
AU - Chastang, Julien
AU - Howard, Daniel
AU - Holt, Benjamin
AU - Gangodagamage, Chandana
AU - Zhao, Peisheng
AU - Rivas, Pablo
AU - Chester, Zachary
AU - Orduz, Javier
AU - John, Aji
N1 - Publisher Copyright:
© 2022
PY - 2022/2
Y1 - 2022/2
N2 - In 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 pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future.
AB - In 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 pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future.
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U2 - 10.1016/j.cageo.2022.105034
DO - 10.1016/j.cageo.2022.105034
M3 - Article
AN - SCOPUS:85122572031
SN - 0098-3004
VL - 159
JO - Computers and Geosciences
JF - Computers and Geosciences
M1 - 105034
ER -