| --- |
| library_name: tte |
| pipeline_tag: feature-extraction |
| tags: [location-encoder, geospatial, remote-sensing, sentinel-2, voronoi] |
| license: mit |
| --- |
| |
| # Tessellating the Earth (TTE) — location encoder |
|
|
| Maps a geographic coordinate `(lat, lon)` to a learned embedding via a learnable |
| Spherical Voronoi partition of S² with global semantic tokens. ECCV 2026. |
|
|
| Daniel Cher, Hamza Iqbal, Eric Xing, Brian Wei, Nathan Jacobs — Washington University in St. Louis ([MVRL](https://mvrl.cse.wustl.edu/)). |
|
|
| - Code: https://github.com/mvrl/TTE |
| - Project page: https://dcher95.github.io/TTE/ |
|
|
| ```python |
| import torch |
| from tte import TTE |
| |
| model = TTE.from_pretrained("MVRL/TTE").eval() |
| coords = torch.tensor([[37.77, -122.42], |
| [-3.12, 60.02]]) |
| emb = model.encode(coords) |
| ``` |
|
|
| Image backbone used during training (not needed here): frozen SSL4EO-S12 MAE ViT-L/16. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{cher2026tte, |
| title = {Tessellating the Earth: Learnable Spherical Voronoi Partitions for Location Encoding}, |
| author = {Cher, Daniel and Iqbal, Hamza and Xing, Eric and Wei, Brian and Jacobs, Nathan}, |
| booktitle = {European Conference on Computer Vision (ECCV)}, |
| year = {2026} |
| } |
| ``` |
|
|
