SSL4EO-S12
The SSL4EO-S12 dataset is a large-scale multimodal multitemporal dataset for unsupervised/self-supervised pre-training in Earth observation. The dataset consists of unlabeled patch triplets (Sentinel-1 dual-pol SAR, Sentinel-2 top-of-atmosphere multispectral, Sentinel-2 surface reflectance multispectral) from 251079 locations across the globe, each patch covering 2640mx2640m and including four seasonal time stamps.
Access the dataset
- Raw dataset: The full SSL4EO-S12 dataset (1.5TB, 500GB for each modality) is accessible at mediaTUM. There are some void IDs (gaps in folder names), see
data/void_ids.csv. Center coordinates of all locations are available here. - Example subset: An example 100-patch subset (600MB) is available at Google Drive.
- Compressed dataset: A compressed 8-bit version (20-50GB for each modality, including an RGB version) is available at mediaTUM. The raw 16/32-bit values are normalized by mean and std and converted to uint8, plus a default geotiff JPEG compression with quality 75. Note: in our experiments, 8-bit input (without JPEG compression) performs comparably well as 16-bit.
- A 50k (random) RGB subset (18GB) is available here (link broken). Sample IDs see
data/50k_ids_random.csv.
Collect your own data
Check src/download_data for instructions to download sentinel or other products from Google Earth Engine.
Pre-trained models
The pre-trained models with different SSL methods are provided as follows (13 bands of S2-L1C, 100 epochs, input clip to [0,1] by dividing 10000).
| SSL method | Arch | BigEarthNet* | EuroSAT | So2Sat-LCZ42 | Download | Usage | ||
|---|---|---|---|---|---|---|---|---|
| MoCo | ResNet50 | 91.8% | 99.1% | 60.9% | full ckpt | backbone | logs | define model, load weights |
| MoCo | ViT-S/16 | 89.9% | 98.6% | 61.6% | full ckpt | backbone | logs | define model, load weights |
| DINO | ResNet50 | 90.7% | 99.1% | 63.6% | full ckpt | backbone | logs | define model, load weights |
| DINO | ViT-S/16 | 90.5% | 99.0% | 62.2% | full ckpt | backbone | logs | define model, load weights |
| MAE | ViT-S/16 | 88.9% | 98.7% | 63.9% | full ckpt | backbone | logs | define model, load weights |
| Data2vec | ViT-S/16 | 90.3% | 99.1% | 64.8% | full ckpt | backbone | logs | define model, load weights |
* Note the results for BigEarthNet are based on the train/val split following SeCo.
Other pre-trained models:
| SSL method | Arch | Input | Download | ||
|---|---|---|---|---|---|
| MoCo | ResNet18 | S2-L1C 13 bands | full ckpt | backbone | logs |
| ResNet18 | S2-L1C RGB | full ckpt, full ckpt ep200 | backbone | logs | |
| ResNet50 | S2-L1C RGB | full ckpt | backbone | logs | |
| ResNet50 | S1 SAR 2 bands | full ckpt | backbone | logs | |
| MAE | ViT-S/16 | S1 SAR 2 bands | full ckpt | backbone | |
| ViT-B/16 | S1 SAR 2 bands | full ckpt | backbone | ||
| ViT-L/16 | S1 SAR 2 bands | full ckpt | backbone | ||
| ViT-H/14 | S1 SAR 2 bands | full ckpt | backbone | ||
| ViT-B/16 | S2-L1C 13 bands | full ckpt | backbone | ||
| ViT-L/16 | S2-L1C 13 bands | full ckpt | backbone | ||
| ViT-H/14 | S2-L1C 13 bands | full ckpt | backbone |
License
This repository is released under the Apache 2.0 license. The dataset and pretrained model weights are released under the CC-BY-4.0 license.
Citation
@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}
}