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# Purpose

`eo-data-embedding` is a multi-modal geospatial **embedding search & change-detection** toolkit
built on a frozen Vision-Transformer foundation model (Clay v1.5). It turns Sentinel-1/2 imagery
into reusable vector embeddings and exposes four capabilities over them:

- **Similarity search** — find look-alike scenes/tiles by cosine similarity (FAISS).
- **Few-shot classification** — a linear probe on frozen embeddings that reaches strong accuracy
  with very few labels per class (vs a from-scratch CNN baseline) — the foundation-model
  **label-efficiency** benefit.
- **Bitemporal change detection** — zero-training Δembedding distance and a supervised change probe.
- **Plug-and-play CPU demo** — a Gradio UI requiring no GPU and no model at runtime.

```{figure} /_static/results/demo_search.png
:width: 95%
:align: center

Similarity search in action — each query tile (left) and its nearest neighbours
retrieved by cosine search over frozen Clay v1.5 embeddings (EuroSAT). See the
[test report](../suitr) for the per-class retrieval, confusion-matrix and
label-efficiency figures.
```

**Benefits:** embed once (the only heavy/GPU step), then run every downstream task cheaply over the
stored embeddings; label-light adaptation; reproducible, config-driven runs; portable across
laptop/CPU, Colab, Kaggle and cloud.