# Future work This page records the follow-on work scoped for `eo-data-embedding`. Every item below is **method- and data-identified**, not vague "future work": each names the published approach and the dataset that would carry it. The common reason none are done here is a single, *external* constraint rather than a design gap — the project ran into a hard GPU **quota/capacity wall** (single GPU slot, `GPUS_ALL_REGIONS = 1`, auto-denied quota bump, Spot preemption, on-demand T4 stockouts across EU zones) and deliberately pivoted to a CPU path to keep delivery honest (see the V&V report `compliance/drd/vv-report.md` §3 and `research/07-engineering-notes.md`). What follows is what the roadmap opens up once accelerator and storage are no longer the binding constraint. The items are ordered by how directly they attack a verified limitation; the first two close open requirement items in the requirement verification status of the V&V report (`compliance/drd/vv-report.md` §4). ## 1. Archive-scale embedding index — the "ML at scale" thesis The published index is built over a **2,000-tile EuroSAT subset**; the "find scenes like this *across the archive*" claim is therefore demonstrated, not stress-tested. With accelerator time for the one-time encode pass and storage for the resulting vector store, the index would scale to a real corpus: - **Data.** `SSL4EO-S12` (~1M+ unlabelled S1+S2 patches — the designated "scale" corpus, see `research/01-datasets.md`) and `BigEarthNet-MM` (~549k paired patches). - **Method.** Embed once with frozen Clay v1.5, persist millions of 1024-d vectors to the parquet store, and back the FAISS index with an ANN structure (IVF/HNSW/PQ) sized for the corpus rather than the exact `IndexFlatIP` used at demo scale. - **What it proves.** Retrieval quality and latency at archive scale — the load-bearing evidence behind **REQ-F-03** beyond the EuroSAT smoke set. - **Why blocked.** The heavy encode pass needs sustained GPU the trial quota never granted; the vector store needs storage well past the demo bundle. ## 2. Time-series change detection — the open scientific frontier This is the **#1 open item**: it closes the phenological-layer limitation behind the **partially verified REQ-F-05**. Zero-training two-date embedding distance is *rejected by experiment* (ROC-AUC at or below chance) because of a seasonality/phenology confound — unchanged vegetated tiles move *more* in embedding space than truly changed urban ones, and the **phenological** layer (genuinely different ground cover, not a colour shift) cannot be normalized away or resolved with only two dates (V&V report §3; `research/06-change-analysis.md` §8–9). - **Method.** Model a location's embedding **trajectory** across many dates (periodic regression / change-point detection); real change is a departure from the seasonal cycle. This is the literature's answer — SeCo's motivation, Element84's periodic-outlier approach, [OPTIMUS](https://arxiv.org/abs/2506.13902)'s change-point framing. - **Data.** Multi-date stacks that OSCD's two dates cannot supply — [SpaceNet 7](https://spacenet.ai/sn7-challenge/) (monthly mosaics with building-change labels) or DynamicEarthNet (daily Planet imagery, monthly semantic labels), or raw Sentinel-2 stacks via the Microsoft Planetary Computer. - **Why blocked.** Embedding every date multiplies the encode cost, and multi-date stacks multiply storage — exactly the two resources the trial setup lacked. ## 3. Seasonally-invariant or better-matched encoders The frozen-FM thesis was tested with one backbone (Clay v1.5). A natural ablation swaps or compares against encoders pretrained with **seasonal-contrast** objectives so that phenology is suppressed *in the embedding* rather than learned around downstream. - **Method.** Compare frozen Clay against SeCo / CaCo (seasonal contrast) and against Prithvi / DOFA-CLIP, holding the rest of the pipeline fixed. - **What it proves.** Whether the label-efficiency and change-detection results are a property of the *thesis* (any strong frozen FM) or of *Clay specifically* — and whether a seasonally-invariant backbone recovers the change signal that §2 attacks from the data side. - **Why blocked.** Loading and running several large FMs over the evaluation sets is GPU-bound. ## 4. Trained cross-modal alignment (SAR ↔ optical) Clay's frozen embeddings are only **weakly cross-modal** (SAR→optical P@1 = 0.042, ~5× chance) because Clay has no cross-modal training objective. The project's honest result is that a single learned **1024×1024 linear map** on just 180 pairs already lifts this to P@1 = 0.142 (~17× chance, median rank 32→8): the modalities are *linearly relatable* without joint training (V&V report §3). - **Method.** Replace the small linear map with a properly **contrastive cross-modal alignment** trained on a large paired corpus (BigEarthNet-MM, SSL4EO-S12), targeting the DOFA-CLIP band of purpose-built cross-modal retrieval. - **Why blocked.** Contrastive training over a large paired corpus needs both the accelerator and the corpus storage. ## 5. Frozen-vs-fine-tuned ablation By design the encoder is never trained — the frozen pass is the project's load-bearing decision (see the [design overview](overview)). A bounded ablation would quantify the ceiling that decision leaves on the table. - **Method.** Compare the frozen probe against **lightweight fine-tuning** (e.g. LoRA adapters on the encoder) on the same splits and seeds. - **What it proves.** The size of the frozen-vs-tuned gap — sharpening the "frozen embeddings buy label-efficiency, not supremacy" finding (at the full label pool the from-scratch CNN already wins 0.949 vs 0.920). - **Why blocked.** Fine-tuning, even parameter-efficient, reintroduces the GPU requirement the project pivoted away from. ## Summary None of the above is blocked by the software design: the pipeline already threads `device` end to end (`map_location` + `.to(device)`), the store and index are corpus-agnostic, and the encoder is swappable behind the `embed` interface. The binding constraints were **GPU quota** and **storage**, recorded plainly rather than hidden. With those removed, items 1–2 are the highest-value next steps — they close open verification items (**REQ-F-03** at scale, **REQ-F-05**'s phenological layer) — while 3–5 deepen the scientific claims the project already reports honestly.