---
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}
}
```