Using Deep Learning to Predict Wildfires with Feng Yan

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Feng Yan, Assistant Professor at the University of Nevada, Reno.

Feng led a session at re:Invent on ALERTWildfire, a camera-based network infrastructure that captures satellite imagery of wildfires. ALERTWildfire was built to serve a few purposes, including the discovery of wildfires, the ability to scale resources accordingly, monitoring the behavior of said fires, assist in evacuations, and ensure contained fires are thoroughly monitored until completely put out. In our conversation, Feng details the development of the machine learning models and surrounding infrastructure used in ALERTWildfire. We also talk through problem formulation, challenges with using camera and satellite data in this use case, and how he has combined the use of Infra-as-a-Service and Function-as-a-Service tools for cost-effectiveness and scalability.

Thanks to our sponsor!

I’d like to thank our friends at Capital One for sponsoring our re:Invent series. Capital One has been a huge friend and supporter of the podcast for some time now, and I’m looking forward to sharing my interview with Dave Castillo, Capital One’s Managing VP of Machine Learning with you on Thursday. Dave and I discuss the unique approach being taken at the company’s “Center for Machine Learning,” as well as some interesting AI use cases being developed at the bank, and the platform they’re building to support their ML and AI efforts.

To learn more about Capital One’s Machine Learning and AI efforts and research, visit

About Feng

Mentioned in the Interview

  • ALERTWildfire
  • ALERTWildfire
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    “More On That Later” by Lee Rosevere licensed under CC By 4.0

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