Robustness to Geographic Distribution Shift using Location Encoders

Geographic distribution shift arises when the distribution of locations on Earth in a training dataset is different from what is seen at test time. The most common approaches to tackling geographic distribution shift treat regions delimited by administrative boundaries such as countries or continents as separate domains and apply standard domain adaptation methods, ignoring geographic coordinates that are often available as metadata. This paper proposes the use of location encoders for training models that are more robust to geographic distribution shift. We show how both simple sine-cosine encoders and pre-trained location encoders can be used to improve standard domain adaptation methods for the special case of geographic distribution shift. Our proposed methods achieve state-of-the-art results on geo-tagged imagery datasets from the WILDS benchmark.