Major TOM Elliot-Pretrain

A unified multi-modal Earth Observation pre-training dataset combining Sentinel-2, Landsat 8/9, Copernicus DEM, and ESA WorldCover on a global 10 km grid. 250,000 tiles, TACO v3 format.

Product Details

Visibility: Public
Owner: Major TOM
Created: 11 Apr 2026
Last Updated: 14 Apr 2026

Product Contents

root

README.md

Product Details

Product Contents

Major TOM Elliot-Pretrain

A unified multi-modal Earth Observation pre-training dataset combining Sentinel-2, Landsat 8/9, Copernicus DEM, and ESA WorldCover on a global 10 km grid. 250,000 tiles, TACO v3 format.

Product Details

Visibility: Public
Owner: Major TOM
Created: 11 Apr 2026
Last Updated: 14 Apr 2026

Product Contents

root

README.md

Name: README.md
Size: 3.94 KB
Content Type: text/markdown
Last Modified: 14 Apr 2026
Source URL: https://data.source.coop/major-tom/core/README.md
Cloud URI: s3://us-west-2.opendata.source.coop/major-tom/core/README.md

Download

Name: README.md
Size: 3.94 KB
Content Type: text/markdown
Last Modified: 14 Apr 2026
Source URL: https://data.source.coop/major-tom/core/README.md
Cloud URI: s3://us-west-2.opendata.source.coop/major-tom/core/README.md

Download

Major TOM Elliot-Pretrain

MajorTOM Elliot-Pretrain is the first expansion of Major TOM focused on fast pre-training of multi-modal AI models on Major TOM data.

It's built on top of the original MajorTOM global 10 km grid
The files come in 10.56 km footprints with harmonised resolution for faster access
It combines four modalities into a single collection where every tile contains co-registered optical, thermal, elevation, and land cover data

This dataset is designed to grow incrementally -- we start with 250,000 monotemporal tiles and are going to add time-series data in the near future. The dataset follows the TACO v3 specification, a format for organizing AI-ready Earth Observation datasets.

Modality	Source	Resolution	Bands
Sentinel-2 L1C	ESA Copernicus	10 m	13 spectral
Landsat 8/9 OLI-TIRS	USGS	30 m	11 (9 OLI + 2 TIRS)
Copernicus DEM GLO-30	ESA / TanDEM-X	30 m	1 (elevation)
ESA WorldCover	ESA	10 m	1 (land cover class)

Quick Start

Pick a tile index and visualize all four modalities:

Reproducible Example

A complete notebook with metadata queries, filtering, and a streaming PyTorch DataLoader with parallel fetching is available here:

example.ipynb

Dataset Structure

Citation

License

CC-BY-SA-4.0

Acknowledgements

MajorTOM-Core has been made possible thanks to Asterisk Labs, the ELLIOT project (European Commission, Horizon Europe, Grant 101214398), and the Image and Signal Processing Group (ISP) at Universitat de Valencia.

Major TOM Elliot-Pretrain

MajorTOM Elliot-Pretrain is the first expansion of Major TOM focused on fast pre-training of multi-modal AI models on Major TOM data.

It's built on top of the original MajorTOM global 10 km grid
The files come in 10.56 km footprints with harmonised resolution for faster access
It combines four modalities into a single collection where every tile contains co-registered optical, thermal, elevation, and land cover data

Modality	Source	Resolution	Bands
Sentinel-2 L1C	ESA Copernicus	10 m	13 spectral
Landsat 8/9 OLI-TIRS	USGS	30 m	11 (9 OLI + 2 TIRS)
Copernicus DEM GLO-30	ESA / TanDEM-X	30 m	1 (elevation)
ESA WorldCover	ESA	10 m	1 (land cover class)

Quick Start

Pick a tile index and visualize all four modalities:

Reproducible Example

A complete notebook with metadata queries, filtering, and a streaming PyTorch DataLoader with parallel fetching is available here:

example.ipynb

Dataset Structure

Citation

License

CC-BY-SA-4.0

Acknowledgements

1majortom-core/
2|-- COLLECTION.json
3|-- METADATA/
4|   |-- collection.parquet
5|   |-- sample.parquet
6|   |-- sample__s2.parquet
7|   |-- sample__l8.parquet
8|   |-- sample__dem.parquet
9|   |-- sample__wc.parquet
10|-- DATA/
11    |-- {tile_id}/
12        |-- s2/data.tif
13        |-- l8/data.tif
14        |-- dem/data.tif
15        |-- wc/data.tif

1majortom-core/
2|-- COLLECTION.json
3|-- METADATA/
4|   |-- collection.parquet
5|   |-- sample.parquet
6|   |-- sample__s2.parquet
7|   |-- sample__l8.parquet
8|   |-- sample__dem.parquet
9|   |-- sample__wc.parquet
10|-- DATA/
11    |-- {tile_id}/
12        |-- s2/data.tif
13        |-- l8/data.tif
14        |-- dem/data.tif
15        |-- wc/data.tif

1import numpy as np
2import rasterio
3import matplotlib.pyplot as plt
4
5BASE = "https://data.source.coop/major-tom/core"
6IDX = 42  # <- change this to explore different tiles
7
8MODS = ["s2", "l8", "dem", "wc"]
9fig, axes = plt.subplots(1, 4, figsize=(20, 5))
10
11for j, mod in enumerate(MODS):
12    url = f"/vsicurl/{BASE}/DATA/{IDX}/{mod}/data.tif"
13    with rasterio.open(url) as src:
14        if mod in ("s2", "l8"):
15            rgb = src.read([4, 3, 2]).astype(np.float32)
16            axes[j].imshow(np.clip(rgb / 3000, 0, 1).transpose(1, 2, 0))
17        elif mod == "dem":
18            axes[j].imshow(src.read(1), cmap="terrain")
19        else:
20            axes[j].imshow(src.read(1))
21    axes[j].set_title(mod)
22    axes[j].axis("off")
23
24plt.tight_layout()
25plt.show()

1import numpy as np
2import rasterio
3import matplotlib.pyplot as plt
4
5BASE = "https://data.source.coop/major-tom/core"
6IDX = 42  # <- change this to explore different tiles
7
8MODS = ["s2", "l8", "dem", "wc"]
9fig, axes = plt.subplots(1, 4, figsize=(20, 5))
10
11for j, mod in enumerate(MODS):
12    url = f"/vsicurl/{BASE}/DATA/{IDX}/{mod}/data.tif"
13    with rasterio.open(url) as src:
14        if mod in ("s2", "l8"):
15            rgb = src.read([4, 3, 2]).astype(np.float32)
16            axes[j].imshow(np.clip(rgb / 3000, 0, 1).transpose(1, 2, 0))
17        elif mod == "dem":
18            axes[j].imshow(src.read(1), cmap="terrain")
19        else:
20            axes[j].imshow(src.read(1))
21    axes[j].set_title(mod)
22    axes[j].axis("off")
23
24plt.tight_layout()
25plt.show()

1@inproceedings{francis2024majortom,
2  title={Major TOM: Expandable Datasets for Earth Observation},
3  author={Francis, Alistair and Czerkawski, Mikolaj},
4  booktitle={IGARSS 2024},
5  pages={2935--2940},
6  year={2024},
7  doi={10.1109/IGARSS53475.2024.10640760}
8}

1@inproceedings{francis2024majortom,
2  title={Major TOM: Expandable Datasets for Earth Observation},
3  author={Francis, Alistair and Czerkawski, Mikolaj},
4  booktitle={IGARSS 2024},
5  pages={2935--2940},
6  year={2024},
7  doi={10.1109/IGARSS53475.2024.10640760}
8}