Organized Machine Learning Summer School

In collaboration with the Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (IEEE GRSS) Earth Science Informatics Technical Committee (ESI TC), Interagency Implementation and Advanced Concepts Team (IMPACT) organized a machine learning summer school session for twenty-six global and diverse students during 5/31-6/3/21. The session on “Scaling Machine Learning for Remote Sensing on Cloud Computing Environment” took place during the 4-day IEEE-GRSS event hosted by the working group on High-performance and Disruptive Computing in Remote Sensing (HDCRS).  The goals of the session included providing technical guidance on performing an end-to-end machine learning for remote sensing, promoting open science via collaboration and training, developing machine learning expertise for remote sensing, providing a platform for sharing experiences and lessons learned, and promoting collaboration amongst machine learning experts, domain experts, and software developers. Participants learned the fundamentals of end-to-end machine learning life cycle by designing, implementing, and deploying deep learning models on the cloud. In the process, they gained insights into cloud computing for machine learning while in an environment that encouraged active participation and the exchange of ideas. IMPACT leveraged NASA’s Space Act Agreement with Amazon to provide Amazon Web Services credits to the participants.  IMPACT team members: Manil Maskey (ESI TC Chair), Iksha Gurung, Muthukumaran Ramasubramanian, Shubhankar Gahlot, and Drew Bollinger each gave lectures during the summer school session.


Resources: Notebooks, Video Recording.

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