A review of Earth Artificial Intelligence

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…

• NS
  National Science Foundation

  Awards: 80 NSSC21K0512, 2126315, 2117834, 1835717, EAR-1947875, 2019609, DE-AC02-05CH11231, 1947893, EAR-1947893
• UD
  U.S. Department of Energy

  Awards: -AC02-05CH11231, 05CH11231, AC02-05CH11231, DE-AC02, DE-AC02-05CH11231, DE-AC02-
• NA
  National Aeronautics and Space Administration

  Awards: DE-AC02-05CH11231, 80NSSC21K0512, 80NM0018D0004, 80NSSC21M0028, 2117834, 80 NSSC21M0028, 80NSSC21M0027
• CI
  California Institute of Technology

  Award: 80NM0018D0004
• KN
  Kansas NSF EPSCoR

  Awards: 80 NSSC21M0027, 2019609
• NS
  Nuclear Safety and Security Commission
• MO
  Ministry of Education, India
• OO
  Office of Science

  Awards: AC02-05CH11231, -AC02-05CH11231, DE-AC02
• BA
  Biological and Environmental Research

  Awards: 05CH11231, 80NM0018D0004, DE-AC02-05CH11231, AC02-05CH11231
• OO
  Office of Experimental Program to Stimulate Competitive Research