Mirror weights from dcher95/TTE; update model card

README.md

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+---
+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}
+}
+```

config.json

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+{
+  "hidden_dim": 512,
+  "n_registers": 64,
+  "n_reslayers": 2,
+  "n_sites": 4096,
+  "normalize": true,
+  "output_dim": 512,
+  "register_fixed_gate": 0.5,
+  "site_embed_dim": 384,
+  "use_cosine_attention": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a3e6fabf7f72d6943887d9cb5fd58ea45138267af31716489616f75e2fd47aae
+size 13814728