Global coverage of the embeddings, coloured by the top-3 principal components of the 768-d UniverSat vectors (mapped to RGB). Distinct colours mark distinct embedding neighbourhoods — deserts (yellow), vegetation (green), ice & boreal regions (cyan).

Core-S2L2A-UniverSat 🛰️

Dataset	Modality	Number of Embeddings	Sensing Type	Embedding Dim	Source Dataset	Source Model	Size
Core-S2L2A-UniverSat	Sentinel-2 Level 2A	2,245,884	Multispectral (L2A surface reflectance)	768	Core-S2L2A	UniverSat	6.4 GB

This dataset provides a dense, global set of whole-image embeddings for the Major TOM Core-S2L2A collection. Each Sentinel-2 Level 2A fragment is encoded into a single 768-dimensional vector with UniverSat, a resolution- and modality-agnostic transformer backbone for Earth Observation. It is an embedding expansion of Major TOM in the spirit of the Major TOM Global Embeddings project.

Content

Field	Type	Description
embedding	array	Raw UniverSat whole-image embedding (768 × float32)
grid_cell	string	Major TOM grid cell (e.g. 922D_249L)
timestamp	string	Sensing timestamp of the source product (e.g. 20230119T161811)
product_id	string	ID of the original Sentinel-2 product

Rows are stored across 20 data-*.parquet shards (~112k rows each), keyed to the source fragment by (grid_cell, product_id, timestamp), so embeddings can be joined back to imagery, geometry, and other Major TOM expansions of Core-S2L2A.

Input Data

• Sentinel-2 (Level 2A) surface-reflectance fragments from Major TOM Core-S2L2A, normalised per band with the standard Major TOM NORM_s2l2a statistics.
• Each fragment is encoded at its native 10 m grid (image size 1068 × 1068 px ≈ a 10.7 km tile), so one embedding summarises the whole fragment — no tiling, no overlap.
• Input patch size: 120 m (≈ an 89 × 89 patch grid); the encoder pools this into a single global descriptor per fragment.

The embeddings build on the Major TOM Core-S2L2A dataset.

Citation

If you use this dataset, please cite UniverSat:

@article{perron2026universat,
  title   = {UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation},
  author  = {Perron, Yohann and Astruc, Guillaume and Gonthier, Nicolas
             and Mallet, Clement and Landrieu, Loic},
  journal = {arXiv preprint arXiv:2606.23503},
  year    = {2026}
}