![\"GitHub](\"https://img.shields.io/github/stars/ESA-PhiLab/Major-TOM?style=social&label=GitHub\")
Major TOM compatible datasets developed externally
\n
\n\nIs Major TOM just another EO dataset?
\n\n Almost. Major TOM is **not a dataset**, but a project aiming to standardize some of the future EO datasets. As an example of what such a dataset could be like, **MajorTOM-Core** is released as a nearly global dataset of Sentinel-2 data.\n \n Scroll up to the 🎞 **Official Image Datasets** section of this file to see the list of current datasets.\n\n
\n\nWho is going to contribute to upcoming Major TOM datasets?
\n \n Anyone can contribute. The original authors of the Major TOM paper are already working on a few other datasets that will join the Major TOM initiative.\n\n
","html":"Can I join Major TOM organisation on HuggingFace?
\n \n Anyone can join the organisation with reading rights. In order to gain contributor rights, you will need to contact one of the admins and verify who you are and how you would like to contribute (you should be allowed to contribute with any dataset that follows Major TOM standard).\n![\"image/png\"](\"https://cdn-uploads.huggingface.co/production/uploads/6304c06eeb6d777a838eab63/BJKsLwX0GG4W3-gdf40TJ.png\")
Major TOM: Expandable Datasets for Earth Observation
\nA standard for curating, sharing and combining large-scale EO datasets.
\nThis organisation provides a platform for making further contributions to the project with the aim of building large, open and compatible Earth observation datasets.
\n\n
💬 Discord
\nDedicated channels for Major TOM coordination on satellite-image-deep-learning Discord server:\nhttps://discord.gg/SsUJCDcrcu
\n🎞 Official Image Datasets
\n\n\t\n\t\t
\n
\n| Dataset | \nModality | \nNumber of Patches | \nSensing Type | \nComments | \n
|---|---|---|---|---|
| Core-S2L2A | \nSentinel-2 Level 2A | \n2,245,886 | \nMulti-Spectral | \nGeneral-Purpose Global (about 23 TB) | \n
| Core-S2L1C | \nSentinel-2 Level 1C | \n2,245,886 | \nMulti-Spectral | \nGeneral-Purpose Global (about 23 TB) | \n
| Core-S1RTC | \nSentinel-1 RTC | \n1,469,955 | \nSAR | \nGeneral-Purpose Global (about 16 TB) | \n
| Core-DEM | \nCopernicus DEM 30 | \n1,837,843 | \nDigital Surface Model (DSM) | \nGeneral-Purpose Global (about 1 TB) | \n
📊 Official Embedding Datasets
\n\n\t\n\t\t
\n
\n| Dataset | \nModality | \nNumber of Embeddings | \nSensing Type | \nSource Dataset | \nSource Model | \nSize | \n
|---|---|---|---|---|---|---|
| Core-S2L1C-SSL4EO | \nSentinel-2 Level 1C | \n56,147,150 | \nMulti-Spectral | \nCore-S2L1C | \nSSL4EO-ResNet50-DINO | \n252.9 GB | \n
| Core-S1RTC-SSL4EO | \nSentinel-1 RTC | \n36,748,875 | \nSAR | \nCore-S1RTC | \nSSL4EO-ResNet50-MOCO | \n332.5 GB | \n
| Core-S2RGB-DINOv2 | \nSentinel-2 Level 2A (RGB) | \n56,147,150 | \nTrue Colour | \nCore-S2L2A | \nDINOv2 | \n223.1 GB | \n
| Core-S2RGB-SigLIP | \nSentinel-2 Level 2A (RGB) | \n20,212,974 | \nTrue Colour | \nCore-S2L2A | \nSigLIP-SO400M-384 | \n41.3 GB | \n
| Core-S2L1C-DeCUR | \nSentinel-2 Level 1C | \n56,147,150 | \nMulti-Spectral | \nCore-S2L1C | \nSSL4EO-ResNet50-DeCUR | \n252.9 GB | \n
| Core-S1RTC-DeCUR | \nSentinel-1 RTC | \n36,748,875 | \nSAR | \nCore-S1RTC | \nSSL4EO-ResNet50-DeCUR | \n332.5 GB | \n
| Core-S2L2A-MMEarth | \nSentinel-2 Level 2A (MSI) | \n39,727,477,454 | \nMulti-Spectral | \nCore-S2L2A | \nMMEarth | \n5080 GB | \n
| Core-AlphaEarth-Embeddings | \nMulitmodal | \n71,276,453,136 | \nMultiple | \nAlphaEarth Website | \nAlphaEarth | \n6,070 GB | \n
📌 Open Access Manuscript
\n![\"arxiv\"](\"https://img.shields.io/badge/Open_Access-arxiv:2402.12095-b31b1b\"){kind=icon}
\n
\n\nRead Abstract
\n \n\n\nDeep learning models are increasingly data-hungry, requiring significant resources to collect and compile the datasets needed to train them, with Earth Observation (EO) models being no exception. However, the landscape of datasets in EO is relatively atomised, with interoperability made difficult by diverse formats and data structures. If ever larger datasets are to be built, and duplication of effort minimised, then a shared framework that allows users to combine and access multiple datasets is needed. Here, Major TOM (Terrestrial Observation Metaset) is proposed as this extensible framework. Primarily, it consists of a geographical indexing system based on a set of grid points and a metadata structure that allows multiple datasets with different sources to be merged. Besides the specification of Major TOM as a framework, this work also presents a large, open-access dataset, MajorTOM-Core, which covers the vast majority of the Earth's land surface. This dataset provides the community with both an immediately useful resource, as well as acting as a template for future additions to the Major TOM ecosystem.
\n
![\"image/jpeg\"](\"https://cdn-uploads.huggingface.co/production/uploads/6304c06eeb6d777a838eab63/dfw2xVubATDEGj9--4i1D.jpeg\")
If you found this useful for your research, please cite accordingly as:
\n@inproceedings{Major_TOM_2024,\n author={Francis, Alistair and Czerkawski, Mikolaj},\n booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium}, \n title={Major TOM: Expandable Datasets for Earth Observation}, \n year={2024},\n pages={2935-2940},\n doi={10.1109/IGARSS53475.2024.10640760}\n}\nPowered by Φ-lab, European Space Agency (ESA) 🛰️
\n![](\"https://cdn-uploads.huggingface.co/production/uploads/6304c06eeb6d777a838eab63/O_FV_vc-6IPncHPKnpXYp.png\")
\n
FAQ
\n\n
\n\nIs Major TOM just another EO dataset?
\n\nAlmost. Major TOM is not a dataset, but a project aiming to standardize some of the future EO datasets. As an example of what such a dataset could be like, MajorTOM-Core is released as a nearly global dataset of Sentinel-2 data.
\nScroll up to the 🎞 Official Image Datasets section of this file to see the list of current datasets.
\n\n
\n\nWho is going to contribute to upcoming Major TOM datasets?
\n \nAnyone can contribute. The original authors of the Major TOM paper are already working on a few other datasets that will join the Major TOM initiative.
\n\n
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Can I join Major TOM organisation on HuggingFace?
\n \nAnyone can join the organisation with reading rights. In order to gain contributor rights, you will need to contact one of the admins and verify who you are and how you would like to contribute (you should be allowed to contribute with any dataset that follows Major TOM standard).
\nAI & ML interests
Earth Observation Datasets
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