In this episode I caught up with Adam Stewart, creator of TorchGeo, to hear all the latest updates related to this pivotal piece of geospatial AI software. We discuss TorchGeo’s strong adoption in the geospatial ML community and the upcoming 1.0 release, which will introduce long-awaited time series support. Adam shares insights from a recent software literature review covering available geospatial data handling tools, sampling strategies, and the broader machine learning ecosystem. He also talks about the newly formed Technical Steering Committee, outlining its role in guiding the project’s direction. Other topics include upcoming breaking changes to geospatial datasets and samplers, how TorchGeo integrates with other libraries and tools, the project’s growing community, the role of foundation models in handling diverse geospatial products, the promise of zero-shot learning for effortless data labelling, and why no single model can dominate across all domains.
Bio: Adam J. Stewart's research interests lie at the intersection of machine learning and Earth science, especially remote sensing. He is the creator and lead developer of the popular TorchGeo library, a PyTorch domain library for working with geospatial data and satellite imagery. His current research focuses on building foundation models for multispectral imagery. He received his B.S. from the Department of Earth and Atmospheric Sciences at Cornell University and his Ph.D. from the Department of Computer Science at the University of Illinois Urbana-Champaign. He currently works as a postdoctoral researcher at the Technical University of Munich under the guidance of Prof. Xiaoxiang Zhu.
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