Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces

Location encoding is valuable for a multitude of tasks where both the absolute positions and local contexts (image, text, and other types of metadata) of spatial objects are needed for accurate predictions. However, most existing approaches do not leverage unlabeled data, which is crucial for use cases with limited labels. Furthermore, the availability of large-scale real-world GPS coordinate data demands representation and prediction at global scales. However, existing location encoding models assume that the input coordinates are in Euclidean space, which can lead to modeling errors due to distortions introduced when mapping coordinates from other manifolds (e.g., spherical surfaces) to Euclidean space. We introduceSphere2Vec, a location encoder, which can directly encode spherical coordinates while preserving spherical distances.Sphere2Vecis trained with a self-supervised learning framework which pre-trains deep location representations from unlabeled geo-tagged images with contrastive losses, and then fine-tunes to perform super-vised geographic object classification tasks.Sphere2Vecachieves the performances of state-of-the-art results on various image classification tasks ranging from species, Point of Interest (POI) facade, to remote sensing. The self-supervised pertaining significantly improves the performance ofSphere2Vecespecially when the labeled data is limited