Radiant Earth Foundation is hosting an international expert workshop to discuss how best to use machine learning (ML) techniques on NASA’s Earth Observation (EO) data and address environmental challenges. In particular, generation and usage of training datasets for ML applications using EO will be discussed. Participants of the workshop will evaluate recent advancements, identify existing obstacles and develop a best practices guideline to enhance the adoption of these techniques.   

This workshop is sponsored by the NASA Earth Science Data Systems (ESDS) program.

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Tuesday, January 21 • 4:00pm - 5:00pm
Breakout Session 1 (WG2): Modeling approaches and best practices for building the best and computationally optimum model

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Proposed Questions:
  1. Unlike common ML problems, Earth systems have a probabilistic nature, therefore we need to incorporate this property in our modeling frameworks and develop uncertainty aware models. What are the latest advancements in this direction, and what are the recommendations for new research ideas in this domain?
  2. Multispectral data has more than 3 bands (unlike typical computer vision data) and has a temporal dimension which is very informative for many modeling efforts. What are the best practices in incorporating these data features into ML modeling?
  3. Projections, spatial grids, and temporal revisits are almost never the same across two datasets. How can we as a community enable better interoperability between these data (that sometimes need to be input to a model)? And what are the considerations in building models when doing re-projection or spatial/temporal interpolation?
  4. What are the considerations for model development when transitioning from R&D to production: 
    1. How frequently should we re-train models?
    2. Content drift?
    3. Back-testing, Now-testing?
  5. How can ML models be used to improve specifics of measurement instruments? What are the possibilities for using existing training data and modeling frameworks to inform new data collection strategies? Could that lead to improved accounting of uncertainties in ML applications?

avatar for Dalton Lunga

Dalton Lunga

Lead Machine Learning Scientist, Oak Ridge National Laboratory

Tuesday January 21, 2020 4:00pm - 5:00pm EST
Cosmos Club (Board Room)