Robustness to Geographic Distribution Shift using Location Encoders

2025-03-03

AI Summary

Existing domain adaptation methods for geographic distribution shift—where training and test data exhibit spatially inconsistent distributions—typically partition domains by administrative boundaries, neglecting fine-grained spatial structure encoded in continuous latitude-longitude coordinates. Method: This paper introduces the first end-to-end location-aware feature alignment framework for domain adaptation, systematically integrating explicit positional encodings—namely sinusoidal-cosine encoding and pretrained geographic embeddings—into the model architecture. It abandons the discrete administrative boundary assumption and instead directly models continuous spatial relationships over the Earth’s surface. Contribution/Results: Evaluated on the WILDS geographically distributed image benchmark, our approach significantly improves generalization across continents and climate zones. It achieves an average accuracy gain of 3.2–5.7 percentage points over state-of-the-art methods, establishing a scalable, geography-informed positional encoding paradigm for spatially aware machine learning.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Addresses geographic distribution shift in training and test datasets.
• Proposes location encoders to enhance model robustness to geographic shifts.
• Improves domain adaptation methods using geographic metadata for better accuracy.

Innovation

• Uses location encoders for geographic robustness
• Improves domain adaptation with sine-cosine encoders
• Achieves top results on WILDS benchmark datasets