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For over 20 years, ESIP meetings have brought together the most innovative thinkers and leaders around Earth science data, forming a community dedicated to making Earth science data more discoverable, accessible and useful to researchers, practitioners, policymakers, and the public. The theme of the July meeting is "Data for All People: From Generation to Use and Understanding."

Registered attendees can join us virtually at https://2022julyesipmeeting.qiqochat.com/.
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Friday, July 22 • 11:00am - 12:30pm
AI for All People: How to make AI useful for Earth science applications?

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Earth science applications of artificial intelligence and machine learning (AI/ML) have seen a flurry of interest in recent years, as models become more effective at predicting patterns and processes across multiple scales. However, despite this recent focus there still exist a number of common challenges in the development, deployment, and assessment of AI/ML projects which can hinder their usefulness in various domains. In order to fully realize the potential of AI/ML as a practical tool for approaching Earth science problems, practitioners will need to better understand and address these common challenges in a standard, cross-domain way.

This session, organized as part of the ESIP Machine Learning cluster’s ongoing Practical AI initiative, will bring together AI/ML practitioners and users to talk about the generation, use, and understanding of AI/ML systems in the Earth sciences. Talks will focus on practical, successful, and useful applied AI/ML systems, and the approaches taken to overcome the common challenges inherent in producing AI/ML solutions. The session will additionally inform the ongoing Machine Learning cluster white paper, Practical AI for Geospatial Data-driven Applied Sciences, by highlighting the commonalities between successful practical AI initiatives and the “gaps” still to be solved in years to come. Here is the agenda: 
  • Amruta Kale, Marshall Ma - Explainable AI and Provenance in Earth AI Applications
  • Michael Mahoney - AI Use Case on Tree Quantification
  • Doug Newman - AI and NASA Systems
  • Chung Nga - Cloud-based Data Match-Up Service and AI in Oceanography
  • Ziheng Sun - Geoweaver for Productivity and Reusability of AI for Earth scientific workflows 

avatar for Ziheng Sun

Ziheng Sun

Research Assistant Professor, George Mason University
My research interests are mainly on geospatial cyberinfrastructure and machine learning in atmospheric and agricultural sciences.
avatar for Marshall Ma

Marshall Ma

Assistant Professor, University of Idaho
Xiaogang (Marshall) Ma is an assistant professor of computer science at the University of Idaho. He received his Ph.D. degree of Earth Systems Science and GIScience from University of Twente, Netherlands in 2011, and then completed postdoctoral training of Data Science at Rensselaer... Read More →
avatar for Annie Burgess

Annie Burgess

Lab Director, ESIP
avatar for Doug Newman

Doug Newman

Systems Engineer, NASA ESDIS
avatar for Douglas Rao

Douglas Rao

I am currently a Postdoctoral Research Scholar at North Carolina Institute for Climate Studies, also affiliated with NOAA National Centers for Environmental Information. My current research at NCICS focuses on generating a blended near-surface air temperature dataset by integrating... Read More →
avatar for Michael Mahoney

Michael Mahoney

Open Source Intern, RStudio

Friday July 22, 2022 11:00am - 12:30pm EDT
Ballroom 2 600 Commonwealth Pl, Pittsburgh, PA 15222