Bayes Studio at IEEE CCECE 2025: Applied AI & Wildfire Robotics

A Bayes Studio engineer presenting a deep learning architecture diagram showing optical, thermal, and atmospheric gas data pipelines feeding a Feature Pyramid Network (FPN) and multi-scale YOLO loss detection heads.

We were honoured to present at the IEEE CCECE 2025 conference last week, hosted at The University of British Columbia. It was a great opportunity to share Bayes Studio’s latest work on AI-driven wildfire detection and monitoring, combining robotics, machine learning, and environmental resilience.
A big thank you to Michał Aibin, Ph.D. and Northeastern University Vancouver for the opportunity to be part of such a meaningful event. It was inspiring to connect with fellow researchers, industry leaders, and students all working toward innovative climate solutions.

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