🎙️ Revolutionizing Gas Leak Detection with Visual AI & Machine Learning

In this episode of The Empirical Energy Podcast, host Mark Smith sits down with Jae Yoon Chung, Machine Learning Engineer at Clean Connect, to explore how visual AI is transforming gas leak detection in the energy industry.

Jae breaks down the real-world challenges of detecting methane and gas leaks using vision-based models — especially in harsh outdoor environments with wind, rain, snow, and limited edge-device compute. He introduces a breakthrough approach called channel stacking, a method that captures gas movement using just three consecutive frames to dramatically improve detection accuracy while reducing computational load and false alarms.

The conversation goes beyond theory, offering a behind-the-scenes look at how AI models are trained, optimized, and deployed at the edge — and where the technology is headed next. From edge computing to large language models (LLMs) and object-level incident classification, this episode highlights how AI, blockchain, and verification are reshaping the future of global energy markets.

⚡ If you work in energy, AI, emissions monitoring, or industrial technology, this episode is a must-listen.

⏱️ Episode Chapters

00:00 – Introduction to The Empirical Energy Podcast

00:57 – Meet the Guest: Jae Yoon Chung from Clean Connect

01:39 – Machine Learning Challenges in Energy Environments

03:44 – Innovations in Visual Gas Leak Detection

07:29 – Technical Deep Dive: Channel Stacking Explained

14:23 – The Future of Visual AI & LLM Integration

18:53 – Final Thoughts & Call to Action

🎧 Listen & Watch

▶️ YouTube: https://youtu.be/WOejlQSdr_g

🎙 Apple Podcasts: https://podcasts.apple.com/us/podcast/the-empirical-energy-podcast/id1822839881

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