Using Machine Learning To Keep An Eye On The Planet

Summary
Satellite imagery has given us a new perspective on our world, but it is limited by the field of view for the cameras. Synthetic Aperture Radar (SAR) allows for collecting images through clouds and in the dark, giving us a more consistent means of collecting data. In order to identify interesting details in such a vast amount of data it is necessary to use the power of machine learning. ICEYE has a fleet of satellites continuously collecting information about our planet. In this episode Tapio Friberg shares how they are applying ML to that data set to provide useful insights about fires, floods, and other terrestrial phenomena.
Announcements

• Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
• Your host is Tobias Macey and today I'm interviewing Tapio Friberg about building machine learning applications on top of SAR (Synthetic Aperture Radar) data to generate insights about our planet

Interview

• Introduction
• How did you get involved in machine learning?
• Can you describe what ICEYE is and the story behind it?
• What are some of the applications of ML at ICEYE?
• What are some of the ways that SAR data poses a unique challenge to ML applications?
• What are some of the elements of the ML workflow that you are able to use "off the shelf" and where are the areas that you have had to build custom solutions?
• Can you share the structure of your engineering team and the role that the ML function plays in the larger organization?
• What does the end-to-end workflow for your ML model development and deployment look like? 
  • What are the operational requirements for your models? (e.g. batch execution, real-time, interactive inference, etc.)
• In the model definitions, what are the elements of the source domain that create the largest challenges? (e.g. noise from backscatter, variance in resolution, etc.)
• Once you have an output from an ML model how do you manage mapping between data domains to reflect insights from SAR sources onto a human understandable representation?
• Given that SAR data and earth imaging is still a very niche domain, how does that influence your ability to hire for open positions and the ways that you think about your contributions to the overall ML ecosystem?
• How can your work on using SAR as a representation of physical attributes help to improve capabilities in e.g. LIDAR, computer vision, etc.?
• What are the most interesting, innovative, or unexpected ways that you have seen ICEYE and SAR data used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on ML for SAR data?
• What do you have planned for the future of ML applications at ICEYE?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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Links

• ICEYE
• SAR == Synthetic Aperture Radar
• Transfer Learning

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0