Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning
This work addresses the bottleneck of limited labeled data for researchers and practitioners in geospatial machine learning, but it appears incremental as it applies existing methods to new benchmarks.
The paper tackled the problem of labeled data scarcity in remote sensing machine learning by applying domain adaptation to geospatial benchmarks, identifying unique challenges and proposing solutions.
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.