Lost and Found: How Self-Supervised Learning Helps GPS Coordinates Find Their Way

GPS coordinates are a fundamental aspect of location-based applications, yet prior methods for representing them do not fully capture the intricate relationships between different locations. In this paper, we propose a novel map-based approach to embedding GPS coordinates using self-supervised learning. Unlike most prior studies that directly embed GPS coordinates to a latent space, we leverage a map-based approach, allowing embeddings to capture geographical and economic features. Namely, we use a student-teacher architecture, where a student network is trained to mimic the outputs of the teacher, using two different augmented versions of the same input. To capture the rich underlying semantics of GPS coordinates, we further leverage auxiliary tasks including \textit{geo} prediction, high-level reconstruction, and intermediate clustering. The intermediate clustering loss facilitates learning features at different levels of granularity, while the high-level reconstruction loss encourages “local-to-global” correspondences. We evaluate our method on a large-scale dataset of GPS coordinates and demonstrate that it outperforms several baseline methods in terms of the quality of the learned embeddings. Moreover, we show the usefulness of our embeddings in various downstream tasks, such as predicting land price, land cover type, or water quality indice.