# Mapping The World at Taylor Geospatial Page: https://stenobird.com/podcast/satellite-image-deep-learning-5989248/mapping-the-world-at-taylor-geospatial Text version: https://stenobird.com/podcast/satellite-image-deep-learning-5989248/mapping-the-world-at-taylor-geospatial.md Podcast: [Satellite image deep learning](https://stenobird.com/podcast/satellite-image-deep-learning-5989248) Published: 2026-06-10T08:08:35+00:00 Episode link: https://www.satellite-image-deep-learning.com/p/mapping-the-world-at-taylor-geospatial Audio file: https://api.substack.com/feed/podcast/198720648/6d328fc2d5851c97070ad6d13cb966fb.mp3 Processing state: not_requested JSON: https://stenobird.com/v1/public/podcasts/satellite-image-deep-learning-5989248/episodes/mapping-the-world-at-taylor-geospatial Duration seconds: 2041 ## Resource In this episode I sat down with Jennifer Marcus and Isaac Corley from Taylor Geospatial to explore Fields of the World - an open initiative to create globally consistent agricultural field boundary datasets from satellite imagery using AI and cloud-native geospatial infrastructure. Taylor Geospatial, a newly formed research organization, is building openly licensed global datasets as foundational public goods. Jen and Isaac explain the motivation behind the project, the challenges of scaling machine learning beyond well-labelled regions, and why openness in datasets, tooling, and intermediate model outputs, is central to their approach.We dive into the technical details behind the first global release: assembling noisy and uneven benchmark datasets from around the world, training models that generalise across diverse agricultural systems, and releasing everything from Sentinel-2 mosaics and raw segmentation probabilities to polygonised field boundaries through Source Cooperative. Along the way, we discuss community-driven improvement loops inspired by OpenStreetMap, the limitations of 10 m imagery for smallholder agriculture, and the importance of pairing academic researchers with engineering teams to rapidly operationalise new methods. Finally, we look ahead to Taylor Geospatial’s next phase - richer agricultural datasets, “Features of the World,” and a benchmarking initiative aimed at improving evaluation standards and reproducibility across geospatial foundation models. * 📺 Video of this conversation on YouTube * 🖥️ Taylor Geospatial website * 🖥️ FTW website Bio: Jennifer Marcus is Vice President of Strategic Innovation Programs at Taylor Geospatial, where she advances partnerships and programs that translate breakthrough geospatial AI research into real-world impac… ## Actions - request_transcript: `POST https://stenobird.com/v1/public/podcasts/satellite-image-deep-learning-5989248/episodes/mapping-the-world-at-taylor-geospatial/transcription-requests` — Idempotently request low-priority transcript generation for this episode. - read_markdown: `GET https://stenobird.com/podcast/satellite-image-deep-learning-5989248/mapping-the-world-at-taylor-geospatial.md` — Read the agent-friendly Markdown representation of this episode resource. A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed. ## Transcript Full transcripts are not published on public pages unless there is a clear rights basis.