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For over 20 years, ESIP meetings have brought together the most innovative thinkers and leaders around Earth observation data, thus forming a community dedicated to making Earth observations more discoverable, accessible and useful to researchers, practitioners, policy makers, and the public. The theme of the meeting is Putting Data to Work: Building Public-Private Partnerships to Increase Resilience & Enhance the Socioeconomic Value of Data.

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Wednesday, July 22 • 4:00pm - 5:30pm
Understanding and Utilizing AI in Data-Driven Earth Science

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Machine learning (ML) is one of the most powerful yet complex tools in the toolboxes of data scientists. The difficulty of leveraging ML models will increase exponentially when the input data complexity increases. On the other hand, Earth data has become more available and accessible now. The Earth scientific research is at some level driven by the collected or simulated datasets. Using ML tools to understand the Earth is a big challenge ahead and appears to be the responsibility of Earth scientists in the next phase. However, many Earth scientists are still learning about ML and figuring how to integrate it into the existing numeric models. This session will invite speakers to talk about their experiences and share the learnt knowledge to help those who were failed or are still trying. This session calls for the presentations on a variety of ML-based Earth research topics including disruptive climate, hurricane, drought, earthquakes, human geography, socioeconomic study, agriculture, or ML-oriented cyberinfrastructure like catalog, tooling, cloud web services, high performance computing, etc. The session aims to help the community accelerate the engagement between AI and Earth data and improve our ability to deliver value-added information faster and more accurate.

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View Presentations: See Session Notes above

Takeaways
​​​​TBD

Speakers
avatar for Annie Burgess

Annie Burgess

Lab Director, ESIP
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 Zhuangfang NaNa Yi

Zhuangfang NaNa Yi

Machine Learning Engineer, Development Seed
DP

Diego Pons

Columbia University


Wednesday July 22, 2020 4:00pm - 5:30pm EDT
Room 9