collection.py

description = """
## Description

### Dataset

A TACO-formatted multimodal dataset built on **Major TOM**. Each sample aligns **Sentinel-2 L2A**, **Sentinel-1 RTC**, **Copernicus DEM 30**, and optional **Major TOM embeddings** under a single grid so patches are spatially matched and ready for training and evaluation.

This dataset assembles co-registered patches from the **Major TOM core datasets** (S2 L2A / S2 L1C / S1 RTC / DEM) and the official **Major TOM embeddings** (SSL4EO, DINOv2, SigLIP, DeCUR, MMEarth, AlphaEarth). Major TOM provides a **geographical indexing grid** and a **metadata structure** to merge multi-source EO data—ideal for large-scale pretraining, representation learning, and multimodal fusion.

**What each sample contains:**
- **S2 L2A (10 m)** — 13 MSI bands (B1–B12 incl. B10), with native 20 m/60 m bands resampled to 10 m for a consistent stack.
- **S1 RTC (10 m)** — backscatter (VV/VH) and optional geometry/angle layers, co-registered to the S2 grid.
- **DEM (30 m → 10 m)** — Copernicus DEM 30 resampled to 10 m, with optional derived slope/aspect.
- **Embeddings (optional)** — one or more per-patch vectors from Major TOM families (e.g., SSL4EO, DINOv2, SigLIP, DeCUR, MMEarth, AlphaEarth).
- **Metadata** — acquisition dates, orbit/pass, QA (e.g., S2 cloud metrics when available), CRS and affine transform, plus upstream lineage.

The dataset inherits **global land coverage** from Major TOM Core and is **extensible** (you can enable/disable modalities and embeddings per tortilla).

### Sensors used

- **Sentinel-2 MSI (L2A/L1C)** — optical multispectral, 13 bands (443–2190 nm) at 10/20/60 m; all represented on a unified 10 m grid.
- **Sentinel-1 RTC** — SAR backscatter (VV/VH) in analysis-ready RTC format at ~10 m.
- **Copernicus DEM 30** — global 30 m elevation resampled to 10 m for alignment.
- **Embeddings** — model-derived features aligned to the same grid (families: SSL4EO, DINOv2, SigLIP, DeCUR, MMEarth; optional AlphaEarth subset).

### Spectral Bands (S2 MSI)

We expose the native Sentinel-2 MSI band set and place all on a unified 10 m grid:

| idx | Band | Name                      | Central λ | Nominal Res. | Notes |
|:---:|:----:|---------------------------|:---------:|:------------:|------|
|  0  | B1   | Coastal Aerosol           | 443 nm    | 60 m         | resampled to 10 m |
|  1  | B2   | Blue                      | 492 nm    | 10 m         |        |
|  2  | B3   | Green                     | 560 nm    | 10 m         |        |
|  3  | B4   | Red                       | 665 nm    | 10 m         |        |
|  4  | B5   | Red Edge 1                | 704 nm    | 20 m         | resampled to 10 m |
|  5  | B6   | Red Edge 2                | 740 nm    | 20 m         | resampled to 10 m |
|  6  | B7   | Red Edge 3                | 783 nm    | 20 m         | resampled to 10 m |
|  7  | B8   | NIR (Broad)               | 833 nm    | 10 m         |        |
|  8  | B8A  | NIR (Narrow)              | 865 nm    | 20 m         | resampled to 10 m |
|  9  | B9   | Water Vapour              | 945 nm    | 60 m         | resampled to 10 m |
| 10  | B10  | Cirrus (WV 1375 nm)       | 1375 nm   | 60 m         | optional for ML |
| 11  | B11  | SWIR 1                    | 1610 nm   | 20 m         | resampled to 10 m |
| 12  | B12  | SWIR 2                    | 2200 nm   | 20 m         | resampled to 10 m |
"""

bibtex_1 = """
@article{MajorTOM2024,
  author  = {Francis, Alistair and Czerkawski, Mikolaj},
  title   = {Major TOM: Expandable Datasets for Earth Observation},
  journal = {arXiv preprint arXiv:2402.12095},
  year    = {2024},
  doi     = {10.1109/IGARSS53475.2024.10640760}
}
"""


bibtex_2 = """
@article{czerkawski2024global,
  title   = {Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space},
  author  = {Czerkawski, Mikolaj and Kluczek, Marcin and Bojanowski, J. and others},
  journal = {arXiv preprint arXiv:2412.05600},
  year    = {2024},
  doi     = {10.48550/arXiv.2412.05600}
}
"""

# Create a collection object with metadata for the dataset
collection_object = tacotoolbox.datamodel.Collection(
    id="majortom-core-combo",
    title="Major TOM Core-Combo (TACO)",
    dataset_version="1.0.0",
    description=description,
    licenses=["refer-to-upstream"],
    extent={
        "spatial": [[-180.0, -90.0, 180.0, 90.0]],  # global coverage (land-focused per upstream)
        "temporal": [["2014-01-01T00:00:00Z", "2025-09-09T00:00:00Z"]]
    },
    providers=[
        {
            "name": "ESA Φ-lab / Major TOM (Hugging Face)",
            "roles": ["producer", "publisher"],
            "links": [
                {"href": "https://huggingface.co/Major-TOM", "rel": "source", "type": "text/html"}
            ],
        },
        {
            "name": "TACO Foundation",
            "roles": ["curator"],
            "links": [
                {"href": "https://huggingface.co/datasets/tacofoundation/", "rel": "homepage", "type": "text/html"}
            ],
        }
    ],
    keywords=["remote-sensing", "earth-observation", "multimodal", "deep-learning",
              "sentinel-2", "sentinel-1", "dem", "embeddings", "taco"],
    task="multimodal-learning",
    curators=[
        {
            "name": "Julio Contreras",
            "organization": "Image & Signal Processing",
            "email": ["julio.contreras@uv.es"],
            "links": [{"href": "https://juliocontrerash.github.io/", "rel": "homepage", "type": "text/html"}],
        },
        {
            "name": "TACO Foundation",
            "organization": "TACO",
            "links": [{"href": "https://huggingface.co/datasets/tacofoundation/", "rel": "homepage", "type": "text/html"}],
        }
    ],
    split_strategy="all-train",
    discuss_link={
        "href": "https://huggingface.co/Major-TOM",
        "rel": "discussion",
        "type": "text/html"
    },
    raw_link={
        "href": "https://huggingface.co/Major-TOM",
        "rel": "source",
        "type": "text/html"
    },
    # Optional domain-specific metadata (mirrors your README sections)
    optical_data={"sensor": "sentinel2msi"},
    radar_data={"sensor": "sentinel1-rtc"},
    elevation_data={"sensor": "cop-dem30"},
    embeddings={
        "families": ["SSL4EO", "DINOv2", "SigLIP", "DeCUR", "MMEarth", "AlphaEarth"],
        "storage": "per-patch vectors (Parquet/NPY sidecars)"
    },
    taco_spec={
        "grid": "Major TOM global grid",
        "patch_size_px": [512, 512],
        "resolution_m": 10,
        "spatial_extent_m": [5160, 5160],
        "assets": {
            "S2_L2A": "GeoTIFF, 13 bands (B1–B12 incl. B10) @10m",
            "S1_RTC": "GeoTIFF, 2–3 bands (VV, VH, optional angle) @10m",
            "DEM":    "GeoTIFF, 1–3 bands (elevation, optional slope/aspect) @10m",
            "EMB":    "Optional per-patch embeddings (Parquet/NPY)"
        }
    },
    labels={
        "label_classes": [],
        "label_description": "No labeled classes. Designed for representation learning and multimodal tasks."
    },
    scientific={
        "doi": "to-be-assigned",
        "citation": "Please cite the Major TOM paper(s) and the IGARSS 2024 proceeding.",
        "summary": "Major TOM Core-Combo reorganizes upstream Major TOM sources (S2/S1/DEM and embeddings) into aligned TACO tortillas for fast, consistent access.",
        "publications": [
            {"doi": "10.48550/arXiv.2402.12095", "citation": bibtex_1, "summary": "arXiv paper introducing the Major TOM framework."},
            {"doi": "10.48550/arXiv.2412.05600", "citation": bibtex_2, "summary": "arXiv on global/dense embeddings aligned to Major TOM grid."}
        ]
    }
)