What does it really take to teach AI to understand our planet?

In this episode, Matt sits down with Isaac Corley, Senior Machine Learning Engineer at Wherobots and maintainer of TorchGeo, and Christopher Ren, Data Scientist and writer behind some of the most thought-provoking essays on Earth observation and AI.

They dive deep into the state of Geospatial AI from embeddings and foundation models to how these tools are reshaping Earth observation and remote sensing. You’ll hear real-world perspectives on what’s working, what’s not, and where the hype outpaces reality.

Key topics covered:

What "embeddings" actually mean for geospatial and remote sensing
How foundation models like AlphaEarth and Tessera are trained
The challenges of applying ML at global scale
The future of Earth observation data pipelines
Why geospatial AI is still the "wild west"

If you’ve ever wondered how AI models are built to map, monitor, and understand the Earth, this conversation breaks it down without the buzzwords.

LINKS:

ISAAC CORLEY

Website: https://isaacc.dev/
LinkedIn: linkedin.com/in/isaaccorley
X: @isaaccorley_
GitHub: @isaaccorley
I’m active in TorchGeo slack: free to join https://join.slack.com/t/torchgeo/shared_invite/zt-22rse667m-eqtCeNW0yI000Tl4B~2PIw

CHRISTOPHER REN

Blog: https://christopherren.substack.com/
Podcast: Spotify | Apple | Youtube
LinkedIn: https://www.linkedin.com/in/christopherren/
Geovibes/Github: https://github.com/cr458/geovibes

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