metadata
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).
- Code: https://github.com/mvrl/TTE
- Project page: https://dcher95.github.io/TTE/
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
@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}
}