README.md

---
license:
  - apache-2.0   # licenses vary per upstream Major TOM datasets (S2/S1/DEM/embeddings)
language:
  - en
tags:
  - remote-sensing
  - earth-observation
  - sentinel-2
  - sentinel-1
  - dem
  - embeddings
  - multimodal
  - deep-learning
  - taco
pretty_name: Major TOM Core-Combo
viewer: false
---

<div style="text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; max-width: 260px; margin: auto; background-color: #f9f9f9;">
  

![Dataset Image](assets/taco.png)

<b><p>This dataset follows the TACO specification.</p></b>

</div>

<br>

# Major TOM Core-Combo (TACO)

![Dataset Image](assets/major_logo.png)

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.



## Description

### Dataset

This dataset packages co-registered patches drawn 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 defines 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).


## Creators

- ESA Φ-lab & collaborators


## Original datasets

See the official **Major TOM** organization for coverage, counts and updates:

- **Major TOM organization overview (Hugging Face)**
- **Core datasets**: Core-S2L2A, Core-S2L1C, Core-S1RTC, Core-DEM  
- **Embedding releases**: Core-S2L1C-SSL4EO, Core-S1RTC-SSL4EO, Core-S2RGB-DINOv2, Core-S2RGB-SigLIP, Core-S2L1C-DeCUR, Core-S1RTC-DeCUR, Core-S2L2A-MMEarth, Core-AlphaEarth-Embeddings  
- **Spec & paper**: “Major TOM: Expandable Datasets for Earth Observation” (arXiv) and the IGARSS 2024 citation (see Publications)

> Note: sizes/coverage and licenses are inherited from upstream. Always consult the upstream dataset cards.


## TACO dataset

**Construction**  
- **Grid & tiling**: adopt Major TOM’s global grid; fixed-size **512 × 512 px @ 10 m** (~5.12 km) windows keyed by grid cell.  
- **Temporal pairing**: select closest-in-time S1/S2 acquisitions per cell (configurable), preferring **clear-sky** S2 for optical stacks.  
- **Embeddings**: if enabled, fetch and store per-patch embedding vectors (Parquet/NPY sidecar) indexed by the tortilla.  
- **Lineage metadata**: upstream identifiers/commits, acquisition timestamps, processing levels, and provenance.

**Default patch geometry**  
- **Spatial extent**: 5160 m × 5160 m  
- **S2 tensor (bands)**: 13 (B1–B12 incl. B10) → **512 × 512 × 13**  
- **S1 tensor (VV/VH)**: 2 (plus optional angle) → **512 × 512 × 2–3**  
- **DEM tensor**: 1 (or +slope/aspect) → **512 × 512 × 1–3**  
- **Embeddings**: 1..N vectors stored as sidecars (per-patch, not per-pixel)


### 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 |

---

## 🔄 Reproducible Example

```python
import tacoreader
import rasterio as rio
import numpy as np
import matplotlib.pyplot as plt

# Load the dataset (replace with the final registry name when published)
ds = tacoreader.load("tacofoundation:majortom-core-combo")

# Read a sample
i = 0
row = ds.read(i)
row_id = ds.iloc[i]["tortilla:id"]

s2_path = row.read("S2_L2A")      # GeoTIFF
s1_path = row.read("S1_RTC")      # GeoTIFF
dem_path = row.read("DEM")        # GeoTIFF
# emb_path = row.read("EMB")      # Optional embeddings (Parquet/NPY)

with rio.open(s2_path) as s2, rio.open(s1_path) as s1, rio.open(dem_path) as dem:
    # Simple S2 RGB (B4,B3,B2)
    rgb = np.stack([s2.read(4), s2.read(3), s2.read(2)], axis=0)
    rgb = np.transpose(rgb, (1,2,0))
    rgb_norm = np.clip(rgb / 2000.0, 0, 1)

    vv = s1.read(1)
    dem_elev = dem.read(1)

fig, ax = plt.subplots(1,3, figsize=(13,4.2))
ax[0].imshow(rgb_norm); ax[0].set_title(f"S2 RGB — {row_id}"); ax[0].axis("off")
ax[1].imshow(vv);       ax[1].set_title("S1 VV"); ax[1].axis("off")
ax[2].imshow(dem_elev); ax[2].set_title("DEM");   ax[2].axis("off")
plt.tight_layout(); plt.show()
```

<center>
  <img src="assets/majortom_combo_example.png" alt="example" width="100%"/>
</center>

## 🛰️ Sensor Information

Sources in this dataset: **sentinel2msi**, **sentinel1-rtc**, **cop-dem30**, and **Major TOM embeddings** (SSL4EO, DINOv2, SigLIP, DeCUR, MMEarth; optional AlphaEarth).

## 🎯 Tasks

General-purpose: **self-supervised pretraining**, **representation learning**, **multimodal fusion**, **semantic segmentation**, **change detection**, **classification**, **retrieval**.

## 📂 Original Data Repositories (Upstream)

* Major TOM organization [page](https://huggingface.co/Major-TOM).

## 💬 Discussion

Use the Major TOM org discussions/Spaces and the **satellite-image-deep-learning Discord** (Major TOM channels) to coordinate contributions and combinations.

## 🔀 Split Strategy

All train.

## 📚 Scientific Publications

### Publication 01

* **arXiv**: *Major TOM: Expandable Datasets for Earth Observation* (Francis & Czerkawski, 2024).
  **BibTeX**:

```bibtex
@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}
}
```


### Publication 02

* **arXiv (2024)**: *Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space*.


```bibtex
    @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{\"A} and others},
    journal={arXiv preprint arXiv:2412.05600},
    year={2024},
    doi= {10.48550/arXiv.2412.05600}
    }
    
```


## 🤝 Data Providers

| **Name**                      | **Role**               | **URL**                                                              |
| :---------------------------- | :--------------------- | :------------------------------------------------------------------- |
| ESA Φ-lab                     | producer / coordinator | [https://philab.esa.int/](https://philab.esa.int/)                   |
| Major TOM (HF)                | publisher (org)        | [https://huggingface.co/Major-TOM](https://huggingface.co/Major-TOM) |
| CloudFerro (embeddings infra) | collaborator           | [https://cloudferro.com/](https://cloudferro.com/)                   |

---

## 🧑‍🔬 Curators

| **Name**        | **Organization**          | **URL**                                                                                            |
| :-------------- | :------------------------ | :------------------------------------------------------------------------------------------------- |
| Julio Contreras | Image & Signal Processing | [https://juliocontrerash.github.io/](https://juliocontrerash.github.io/)                           |
| TACO Foundation | Curation (TACO format)    | [https://huggingface.co/datasets/tacofoundation/](https://huggingface.co/datasets/tacofoundation/) |

---

## 🎞 Official Image Datasets (from Major TOM)

| Dataset    | Modality            | Number of Patches | Sensing Type          | Comments        |
| ---------- | ------------------- | ----------------- | --------------------- | --------------- |
| Core-S2L2A | Sentinel-2 Level 2A | 2,245,886         | Multi-Spectral        | Global (≈23 TB) |
| Core-S2L1C | Sentinel-2 Level 1C | 2,245,886         | Multi-Spectral        | Global (≈23 TB) |
| Core-S1RTC | Sentinel-1 RTC      | 1,469,955         | SAR                   | Global (≈16 TB) |
| Core-DEM   | Copernicus DEM 30   | 1,837,843         | Digital Surface Model | Global (≈1 TB)  |

---

## 📊 Official Embedding Datasets (from Major TOM)

| Dataset                    | Modality             | # Embeddings   | Sensing Type   | Source Dataset | Source Model          | Size     |
| -------------------------- | -------------------- | -------------- | -------------- | -------------- | --------------------- | -------- |
| Core-S2L1C-SSL4EO          | Sentinel-2 Level 1C  | 56,147,150     | Multi-Spectral | Core-S2L1C     | SSL4EO-ResNet50-DINO  | 252.9 GB |
| Core-S1RTC-SSL4EO          | Sentinel-1 RTC       | 36,748,875     | SAR            | Core-S1RTC     | SSL4EO-ResNet50-MOCO  | 332.5 GB |
| Core-S2RGB-DINOv2          | Sentinel-2 L2A (RGB) | 56,147,150     | True Colour    | Core-S2L2A     | DINOv2                | 223.1 GB |
| Core-S2RGB-SigLIP          | Sentinel-2 L2A (RGB) | 20,212,974     | True Colour    | Core-S2L2A     | SigLIP-SO400M-384     | 41.3 GB  |
| Core-S2L1C-DeCUR           | Sentinel-2 Level 1C  | 56,147,150     | Multi-Spectral | Core-S2L1C     | SSL4EO-ResNet50-DeCUR | 252.9 GB |
| Core-S1RTC-DeCUR           | Sentinel-1 RTC       | 36,748,875     | SAR            | Core-S1RTC     | SSL4EO-ResNet50-DeCUR | 332.5 GB |
| Core-S2L2A-MMEarth         | Sentinel-2 L2A (MSI) | 39,727,477,454 | Multi-Spectral | Core-S2L2A     | MMEarth               | 5080 GB  |
| Core-AlphaEarth-Embeddings | Multimodal           | 71,276,453,136 | Multiple       | AlphaEarth     | AlphaEarth            | 6070 GB  |

---


**Key sources for facts & tables:** Major TOM org page with official tables and IGARSS citation (org card), the arXiv paper describing the framework and grid, specific upstream dataset cards (Core-S2L2A / Core-S1RTC / Core-DEM), the AlphaEarth subset card, and ESA Φ-lab’s post on embedding expansions. ([huggingface.co][1], [arxiv.org][2], [philab.esa.int][3])

[1]: https://huggingface.co/Major-TOM "Major-TOM (Major TOM)"
[2]: https://arxiv.org/abs/2402.12095 "Major TOM: Expandable Datasets for Earth Observation"
[3]: https://philab.esa.int/new-ai-powered-insights-with-the-latest-major-tom-embeddings/ "New AI-powered insights with the latest Major TOM embeddings"