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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.

Note: 1) Registration is now closed. 2) Time Zone is editable on the right hand side of the schedule underneath the search box. 3) Access live sessions by clicking links at the bottom of each session description: QiqoChat Link (meeting registration through Cvent required) or YouTube Livestream (view-only participation option). 4) All session materials and recordings will be made available post-meeting. Questions? Send them to staff@esipfed.org.

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Tuesday, July 21 • 2:00pm - 3:30pm
Organizational Strategies, Standards, and Policies for Machine Learning - Charting the Next Step of ESIP Machine Learning Cluster

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Since 2010s, the Earth science community has seen rapid growth in the interests and practice of adopting machine learning in research and discovery. The fast accumulating Earth science data and mature cloud technology have accelerated the growth of machine learning applications in Earth sciences in both academia and government agencies as well as industries.

Until recently, there is still a lack of systematic strategies and community-driven standards to steward and coordinate machine learning applications for Earth sciences. Recently, NOAA Research Council released its strategy for artificial intelligence outlining its vision to “dramatically expand the application of artificial intelligence in every NOAA mission area by improving the efficiency, effectiveness, and coordination of AI development and usage across the agency.” Similarly, other government agencies and private sectors have also formulated their own vision and practice for adopting ML/AI to advance their missions and put data to work.

In this session, we invite representatives from various government agencies and organizations to share their perspectives on adopting machine learning for Earth sciences (ML4ES). The session will have a set of brief presentations from speakers to outline the current landscape of ML4ES and followed by a panel discussion on how the ESIP community can contribute to and shape this landscape.

This stand-alone session will also serve to inform a follow on session, which is a conversation about possible Cluster activities and outputs.

Panelists
Pete Doucette, Integrated Science and Applications Branch, Earth Resources Observation and Science Center, USGS
Eric Kihn, Director, NCEI’s Center for Coasts, Oceans, and Geophysics (CCOG), NOAA
Dan Morris, Program Director, Microsoft AI for Earth
Catherine Nakalembe, Department of Geographical Sciences, University of Maryland, NASA Harvest Project
Dan Pilone, CEO/CTO, Element 84

View Recording
View Session Notes
View Presentations: See attached

Takeaways
  • Big need for ARD, analytics ready data. But what does that mean? Except at the most basic level, readiness is relative to a specific problem
  • Maturity level criteria are needed for both datasets and models: ORL-type assessments, evaluation metrics.
  • We need best practices, standards for putting data into a data lake so that datasets are interoperable. ESIP is best place to develop cross organizational standards!



Speakers
avatar for Dan Pilone

Dan Pilone

Chief Technologist, Element 84
Dan Pilone is CEO/CTO of Element 84 and oversees the architecture, design, and development of Element 84's projects including supporting NASA, the USGS, Stanford University School of Medicine, and commercial clients. He has supported NASA's Earth Observing System for nearly 13 years... Read More →
avatar for Eric Kihn

Eric Kihn

Director, NOAA/NCEI
avatar for Yuhan (Douglas) Rao

Yuhan (Douglas) Rao

Postdoctoral Research Scholar, CISESS/NCICS/NCSU
avatar for Pete Doucette

Pete Doucette

Lead, Science and Applications, USGS / EROS
avatar for Dan Morris

Dan Morris

Principal Scientist, Microsoft
Dan Morris is a Principal Scientist at Microsoft, where he runs the AI for Earth program, focused on accelerating innovation at the intersection of machine learning and environmental sustainability.  His work includes computer vision applications in wildlife conservation, for e... Read More →
avatar for Catherine Nakalembe

Catherine Nakalembe

Assistant Research Professor, University of Maryland
Dr. Nakalembe has broad interest including agriculture and food security, early warning and assessment of disasters with remote sensing. She is the NASA Harvest Africa Program Lead and serves as the Agriculture and Food Security Thematic Lead on the NASA SERVIR Applied Sciences Team... Read More →



Tuesday July 21, 2020 2:00pm - 3:30pm
Room 3

Attendees (87)