Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning (Proposals Track)

John M Lynch (NC State University); Sam Wookey (Masterful AI)

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Unsupervised & Semi-Supervised Learning Ecosystems & Biodiversity Computer Vision & Remote Sensing


Machine learning in remote sensing has matured alongside a proliferation in availability and resolution of geospatial imagery, but its utility is bottlenecked by the need for labeled data. What's more, many labeled geospatial datasets are specific to certain regions, instruments, or extreme weather events. We investigate the application of modern domain-adaptation to multiple proposed geospatial benchmarks, uncovering unique challenges and proposing solutions to them.

Recorded Talk (direct link)