Tesselating The Earth

Geolocation encoders, which map geographic coordinates tolearned representations, are emerging as an effective means of capturingvisual and non-visual characteristics from a latitude-longitude pair alone.However, existing approaches project coordinates onto fixed bases (e.g.,spherical harmonics), allocating representational capacity uniformly anddevoting equal resources to the open ocean and to a developing city. Weintroduce Tessellating the Earth (TTE), a location encoder built fromlearnable Spherical Voronoi partitions that concentrates representationalcapacity where it is needed in a fully differentiable, end-to-end manner.Each Voronoi site carries its own embedding and migrates during train-ing toward discriminative areas. To bridge the gap between local spatialstructure and global semantic understanding, we introduce global seman-tic tokens: a set of shared learnable concept tokens that distill semanticknowledge from the satellite imagery into a compact vocabulary the lo-cation encoder can reference at inference, enabling geographically distantsites covering similar environments to share semantics. TTE sets a newstate of the art for location encoders across a suite of geospatial classifi-cation and regression tasks, and achieves the strongest results when usedas a geographic prior for fine-grained species classification on iNaturalist-2018. Code, and weights are available at https://github.com/mvrl/TTE.