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The University of Illinois

New Frontiers Initiative Webinar and Training Series

March 15, 2022

TorchGeo: Deep Learning with Geospatial Data

by Adam Stewart, New Frontiers Fellow, Department of Computer Science, University of Illinois

8 AM Pacific / 9 AM Mountain / 10 AM Central / 11 AM Eastern for 1 hour.


Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satellite imagery often includes additional spectral bands beyond red, green, and blue and must be joined to other geospatial data sources that can have differing coordinate systems, bounds, and resolutions. To help realize the potential of deep learning for remote sensing applications, we introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides data loaders for a variety of benchmark datasets, composable datasets for generic geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery. TorchGeo is also the first library to provide pre-trained models for multispectral satellite imagery (e.g. models that use all bands from the Sentinel 2 satellites), allowing for advances in transfer learning on downstream remote sensing tasks with limited labeled data. We use TorchGeo to create reproducible benchmark results on existing datasets and benchmark our proposed method for preprocessing geospatial imagery on-the-fly. TorchGeo is open-source and available on GitHub:


Adam J. Stewart is a Ph.D. student at the University of Illinois at Urbana-Champaign. His research focuses on large-scale applications of deep learning in the field of earth science, particularly in the remote sensing domain. Before starting graduate school, Adam was an HPC system administrator at the Laboratory Computing Resource Center at Argonne National Laboratory, where he helped develop the Spack package manager. He received his B.S. in Science of Earth Systems from Cornell University in 2014 with a concentration in Computational Geophysics.

Please register for the webinar.