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."} ] } )