Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 11 Jul 2021.
Builds on
13 of 24 references.
References · 13
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1 more
- 2018Capsules for Object Segmentationcited by 30
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.