| 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 512-dim embedding via a learnable | |
| Spherical Voronoi partition of S² with global semantic tokens. ECCV 2026. | |
| - Code: https://github.com/mvrl/TTE | |
| - Project page: https://dcher95.github.io/TTE/ | |
| ```python | |
| import torch | |
| from tte import TTE # pip install from github.com/mvrl/TTE | |
| model = TTE.from_pretrained("dcher95/TTE").eval() | |
| coords = torch.tensor([[37.77, -122.42], # (lat, lon) in degrees | |
| [-3.12, 60.02]]) | |
| emb = model.encode(coords) # (N, 512), L2-normalized | |
| ``` | |
| Image backbone used during training (not needed here): frozen SSL4EO-S12 MAE ViT-L/16. | |