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.

Back To Schedule
Thursday, January 23 • 9:30am - 11:00am
Breakout Session 4 (WG3): Best practices for sharing and publishing ML applications (model, training data, and results)

Log in to save this to your schedule, view media, leave feedback and see who's attending!

Proposed Questions:
  1. What are the requirements and best practices for documenting and dissemination of training datasets to ensure reproducibility?
  2. What are the resources/repositories that researchers and practitioners can use to register and document training data? This should enable them to assign a DOI to the dataset.
  3. What are the recommended data formats for training data? e.g. traditionally earth system data are stored in NetCDF format, but they are not cloud-friendly and ML-ready.
  4. How to share processed input data? In many cases, the input data are pre-processed and then used as features in developing an ML model. These data sometimes come from huge datasets like re-analysis products. What are the requirements to share the training data in these scenarios to not duplicate the storage of the input data but ensure reproducibility?
  5. What are the tools (if any) for discovering and accessing training data on the cloud environments? If not enough, what are the requirements for such a tool?

avatar for Hamed Alemohammad

Hamed Alemohammad

Chief Data Scientist, Radiant Earth Foundation

Thursday January 23, 2020 9:30am - 11:00am EST
Cosmos Club (Taft Room) 2121 Massachusetts Ave NW, Washington, DC 20008, USA