Agricultural Monitoring with Fields of The World (FTW) (Tutorials Track) Spotlight
Hannah Kerner (Arizona State University); Caleb Robinson (Microsoft); Isaac Corley (Wherobots); Matthias Mohr (Taylor Geospatial Engine); Gedeon Muhawenayo (Arizona State University); Ivan Zvonkov (University of Maryland); Tristan Grupp (World Resources Institute); Nathan Jacobs (Washington University St. Louis)
Abstract
This tutorial demonstrates how to generate field boundaries globally using the Fields of The World dataset, pretrained models, and command line interface (CLI). We then show how to use those boundaries in agricultural monitoring tasks under climate change, including crop type classification and forest loss monitoring. By the end, users will be able to perform the following tasks to support climate change-related decision-making: (1) Extract agricultural field boundaries for any location, (2) Build machine learning models for crop type classification, and (3) Analyze forest loss within agricultural landscapes. By equipping users with the ability to generate field boundaries and link them to climate-relevant monitoring tasks, this tutorial lowers the barrier for researchers, practitioners, and policymakers to access and deploy advanced geospatial AI.