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README.md

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+---
+license: cc-by-4.0
+tags:
+  - earth-observation
+  - remote-sensing
+  - sentinel-2
+  - sentinel-1
+  - embeddings
+  - model-distillation
+  - geospatial
+datasets:
+  - Major-TOM/Core-S2-L1C
+  - Major-TOM/Core-S2-L2A
+  - Major-TOM/Core-S1-RTC
+  - Major-TOM/Core-AlphaEarth-Embeddings
+library_name: betaearth
+pipeline_tag: feature-extraction
+---
+
+# betaearth-segformer
+
+BetaEarth SegFormer-B2 no FiLM (ISPRS baseline) — no timestamp needed
+
+Part of the **BetaEarth** family — fully trainable, without temporal conditioning.
+
+| Metric | Value |
+|--------|-------|
+| Test cosine similarity | 0.88 |
+| LULC downstream accuracy | 0.869 |
+| Trainable parameters | 104.8M |
+| Total parameters | 104.8M |
+| Inputs | S2 L1C+L2A (9ch), S1 RTC (2ch), COP-DEM (1ch) |
+| Output | (H, W, 64) float32, L2-normalised |
+
+## Usage
+
+```bash
+pip install betaearth
+```
+
+```python
+from betaearth import BetaEarth
+
+model = BetaEarth.from_pretrained("asterisk-labs/betaearth-segformer")
+embedding = model.predict(
+    s2_l2a=s2_l2a,   # (9, H, W) uint16
+    s1=s1,            # (2, H, W) float32
+    dem=dem,          # (1, H, W) float32
+    doy=182,
+)
+# embedding: (H, W, 64) numpy array
+```
+
+## All BetaEarth models
+
+| Model | Cos Sim | Params | Best for |
+|-------|---------|--------|----------|
+| [betaearth-segformer-film](asterisk-labs/betaearth-segformer-film) | **0.886** | 0.3M | Best quality |
+| [betaearth-segformer-film-hilr](asterisk-labs/betaearth-segformer-film-hilr) | 0.886 | 0.3M | Alt frozen |
+| [betaearth-segformer](asterisk-labs/betaearth-segformer) | 0.880 | 104.8M | No timestamp |
+| [betaearth-segformer-film-scratch](asterisk-labs/betaearth-segformer-film-scratch) | 0.883 | 104.8M | End-to-end |
+| [betaearth-rgb-only](asterisk-labs/betaearth-rgb-only) | 0.836 | 26.3M | Minimal data |
+
+## Citation
+
+```bibtex
+@inproceedings{czerkawski2026betaearth,
+  title     = {BetaEarth: Emulating Closed-Source Earth Observation Foundation Models Through Their Public Embeddings},
+  author    = {Czerkawski, Mikolaj},
+  booktitle = {ISPRS Congress 2026},
+  year      = {2026}
+}
+```
+
+## License
+
+CC-BY 4.0. Training data attribution: "The AlphaEarth Foundations Satellite Embedding dataset is produced by Google and Google DeepMind."