# Introduction `eo-data-embedding` is not a classical L0/L1/L2 instrument processor; its data-processing model is a **batch-then-serve embedding pipeline**: a one-time, GPU-oriented encode pass turns Sentinel-1/2 imagery into vector embeddings that are persisted once, after which every downstream capability (similarity search, few-shot probe, bitemporal change detection) operates cheaply over the stored embeddings. ## Processing chain ```{mermaid} :caption: Processing chain — a one-time encode pass, then a fan-out to three consumers over the stored embeddings. flowchart LR tile["tile
(C,256,256)"] --> embed["embed
frozen Clay v1.5 ViT
(N,1024)"] embed --> store["store
Parquet"] store --> search["search
FAISS cosine"] store --> probe["probe
few-shot vs CNN"] store --> change["change
Δembedding, bitemporal"] classDef core fill:#e8f5e9,stroke:#2e7d32,color:#11270f; classDef out fill:#fff3e0,stroke:#ef6c00,color:#3a2400; class tile,embed,store core; class search,probe,change out; ``` 1. **Tile** — raw scene `(C,H,W)` (S2 reflectance / S1 backscatter) split into fixed `(C,256,256)` tiles (`change.tile_image`); EuroSAT arrives pre-tiled. 2. **Embed** — `encode` normalizes per verified band stats and runs the frozen encoder under `torch.no_grad()` → class-token `(N,1024)` (or per-patch tokens for spatial change maps). 3. **Store** — one Parquet row per tile (`id, modality, vector[, label]`); reloaded via `load_embeddings` + `stack_vectors` into the dense `(N,1024)` matrix. 4. **Fan-out** — the stored matrix feeds three independent consumers: **search** (FAISS `IndexFlatIP`, cosine), **probe** (few-shot `LogisticRegression` vs a ResNet-18 baseline), **change** (zero-training delta-embedding distance + supervised probe, held-out threshold). Detailed per-module algorithms, parameters and equations are in the [Software Design Document](../sdd/index) and the [Interface Control Document](../icd); this DPM captures the top-down decomposition and processing flow.