Related papers: Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning

Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning

URL: http://arxiv.org/abs/2107.04983v1
Date: Sun, 11 Jul 2021 06:47:20 GMT
Title: Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning
Authors: Jack Lynch and Sam Wookey
Abstract summary: 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.
Score: 0.0
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: 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.

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