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Satellite image deep learning
Insights from the SMAC earthquake detection challenge
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Insights from the SMAC earthquake detection challenge

With Giorgio Morales and Daniele Rege Cambrin

In this episode, I caught up with Daniele Rege Cambrin, the organiser of the SMAC earthquake detection challenge, and Giorgio Morales, its winner. The challenge invited participants to leverage Sentinel 1 satellite imagery to identify earthquake-affected areas and measure the strength of events, while promoting scalable and resource-efficient solutions.

Giorgio shared his innovative approach that secured first place, and we explored the effort behind designing and solving such a meaningful challenge. This conversation provides valuable insights into developing effective solutions and showcases the potential of satellite data in earthquake monitoring.

Bio: Giorgio is a PhD candidate (ABD) in computer science at Montana State University and a current member of the Numerical Intelligent Systems Laboratory (NISL). He holds a BS in mechatronic engineering from the National University of Engineering, Peru, and an MS in computer science from Montana State University, USA. His research interests are Deep Learning, Explainable Machine Learning, Computer Vision, and Precision Agriculture.

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