[2606.27514] Tessellating The Earth
[Submitted on 25 Jun 2026]
Title:Tessellating The Earth
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Abstract:Geolocation encoders, which map geographic coordinates to learned representations, are emerging as an effective means of capturing visual 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 and devoting equal resources to the open ocean and to a developing city. We introduce Tessellating the Earth (TTE), a location encoder built from learnable Spherical Voronoi partitions that concentrates representational capacity where it is needed in a fully differentiable, end-to-end manner. Each Voronoi site carries its own embedding and migrates during training toward discriminative areas. To bridge the gap between local spatial structure and global semantic understanding, we introduce \emph{global semantic tokens}: a set of shared learnable concept tokens that distill semantic knowledge from the satellite imagery into a compact vocabulary the location encoder can reference at inference, enabling geographically distant sites covering similar environments to share semantics. TTE sets a new state of the art for location encoders across a suite of geospatial classification and regression tasks, and achieves the strongest results when used as a geographic prior for fine-grained species classification on iNaturalist-2018. Code, and weights are available at this https URL.
Comments: European Conference on Computer Vision -- ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.27514 [cs.CV]
(or arXiv:2606.27514v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.27514
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Daniel Cher [view email] [v1] Thu, 25 Jun 2026 19:58:13 UTC (13,032 KB)
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