# Mapping South America and Beyond with Fields of The World V2

Page: https://stenobird.com/podcast/satellite-image-deep-learning-5989248/mapping-south-america-and-beyond-with-fields-of-the-world-v2
Text version: https://stenobird.com/podcast/satellite-image-deep-learning-5989248/mapping-south-america-and-beyond-with-fields-of-the-world-v2.md
Podcast: [Satellite image deep learning](https://stenobird.com/podcast/satellite-image-deep-learning-5989248)
Published: 2026-04-01T07:30:34+00:00
Episode link: https://www.satellite-image-deep-learning.com/p/mapping-south-america-and-beyond
Audio file: https://api.substack.com/feed/podcast/192729889/a08a693b8e616ba95072b0ae5fc1a4bc.mp3
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JSON: https://stenobird.com/v1/public/podcasts/satellite-image-deep-learning-5989248/episodes/mapping-south-america-and-beyond-with-fields-of-the-world-v2
Duration seconds: 1831

## Resource

In this episode I sat down with Hannah Kerner and Tristan Grupp to discuss Fields of The World (FTW), an open-source benchmark and ecosystem for global field boundary segmentation from satellite imagery. We explore the core challenge of building models that generalise across vastly different agricultural systems, and why data diversity, rather than model architecture, is often the limiting factor. Hannah and Tristan explain how targeted annotation in underperforming regions can dramatically improve results, how combining global and local training data avoids catastrophic forgetting, and what they learned from large-scale model experimentation. We also dig into practical evaluation beyond standard IOU metrics, including consistency and throughput, and how small modelling choices like boundary loss weighting can have outsized impact on usability. Finally, we cover the growing tooling ecosystem, real-world user feedback, and what’s coming next, including improved models and a global map of predicted field boundaries. * 🖥️ FTW website * 📺 Recording of this conversation on YouTube Bio Hannah: Hannah Kerner is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University. Her research focuses on advancing the foundations and applications of machine learning to foster a more sustainable, responsible, and fair future for all. Her lab’s research topics include machine learning for remote sensing, algorithmic bias, and machine learning theory. She translates research advances to real-world impact through her roles as the AI/Machine Learning Lead for NASA Harvest and NASA Acres, Center Faculty for the ASU Center for Global Discovery and Conservation Science (GDCS), and Research Director for Taylor Geospatial. She has been recognised by m…

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