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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.

The meeting has now ended. Check out the ESIP Summer Meeting Highlights Webinar and learn how to access session materials at https://www.esipfed.org/collaboration-updates/esip-summer-meeting-2020-recap.
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Tuesday, July 21 • 4:00pm - 5:30pm
Organizational Strategies, Standards, and Policies for Machine Learning - Charting the Next Step of ESIP Machine Learning Cluster

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Following on from the prior session, "Organizational Strategies, Standards, and Policies for Machine Learning, Are There Any?", this session is a Machine Learning Cluster conversation and planning meaning to discuss activities or outputs the cluster might produce that support ESIP members in using ML. ESIP has produced a number of influential and important guidelines, incubated and hatched influential tools, provided educational materials and fostered data access and stewardship. There are opportunities in each of these arenas for the ML Cluster to make contributions, as well as others we haven't even thought of yet.

This meeting would be of interest to parties both seeking and offering help, as well as people generally interested in machine learning.

If you are interested in ML and have ideas about how ESIP could be effective in bettering the use of ML in our membership, please attend!

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View Presentations: No slides were presented during this session, though part 1 did include slides - see Session Notes.

  • Create a list of existing Earth Science relevant ML tutorials and tools, including Yuhan’s tutorials.
  • Reach out to ESIP community, ask for existing ML projects that can be captured in training material.
  • Create dataset readiness levels. May initially be different for vector vs raster data, but hope to harmonize. Once this is drafted, reach out to other relevant clusters to understand their problems and refine the readiness levels.

avatar for Douglas Rao

Douglas Rao

Research Scientist, CISESS/NCICS/NCSU
I am currently a Research Scientist at North Carolina Institute for Climate Studies, 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 in situ measurements... Read More →

Tuesday July 21, 2020 4:00pm - 5:30pm EDT
Room 3
  Breakout Session, Room 3