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---
license: cc-by-4.0
pretty_name: SSL4EO-S12-downstream
task_categories:
- other
language: en
tags:
- earth-observation
- satellite-imagery
- sentinel
configs:
- config_name: data_dev
data_files: data_dev/*
---
# SSL4EO-S12-downstream
Welcome to the SSL4EO-S12-downstream dataset. This dataset is used in the [Embed2Scale Challenge](https://eval.ai/web/challenges/challenge-page/2465).
Currently there is only one set of data available `data_dev`, used in the development (dev) phase of the challenge.
The dev set comprises 5149 datacubes of Sentinel-1 and Sentinel-2 data, as visualized below. The data is organized under the `data_dev` folder, which contains one subfolder per modality (s1, s2l1c and s2l2a) where the datacubes reside. Each datacube constitute one location, with S1 VV and VH polarisations, S2 L1C and S2 L2A channels, at four timesteps. For each datacube, there is one sample of all the modalities for the four season: spring, summer, autumn and winter. The datacubes are stored in zipped zarr archives; see [here](https://github.com/DLR-MF-DAS/embed2scale-challenge-supplement/tree/main/data_loading_submission_demo) for instructions how to load the data. The data in the zarr archives is structure as (number of locations, number of timestamps, number of channels, heigh, width) with the dimensions (1, 4, 27, 264, 264); the 27 channels coming from 2 S1 polarizations (VV and VH), 13 S2 L1C channels (B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B10, B11, B12), and 12 S2 L2A channels (B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B11, B12).
<img src="https://raw.githubusercontent.com/DLR-MF-DAS/embed2scale-challenge-supplement/refs/heads/main/figs/Figure_20250219_Albrecht_Eval-AI-Challenge.svg" alt="Technical overview of challenge data and evaluation scheme." style="width: 100%; height: auto;">
The data is structured identically to the SSL4EOS12 v1.1 dataset:
<code>
<pre>
@article{blumenstiel2025ssl4eos12,
title={{SSL4EOS12 v1.1 - A Multimodal, Multiseasonal Dataset for Pretraining}},
author={Blumenstiel, Benedikt and Braham, Nassim Ait Ali and Albrecht, Conrad M and Maurogiovanni, Stefano and Fraccaro, Paolo},
journal={arXiv preprint arXiv:2503.00168},
year={2025}
}
</pre>
</code>
which is based on
<code>
<pre>
@article{wang2022ssl4eo,
title={{SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation}},
author={Wang, Yi and Braham, Nassim Ait Ali and Xiong, Zhitong and Liu, Chenying and Albrecht, Conrad M and Zhu, Xiao Xiang},
journal={arXiv preprint arXiv:2211.07044},
year={2022}
}
</pre>
</code>
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