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Self-supervised pre-training bears potential to generate expressiverepresentations without human annotation. Most pre-training in Earthobservation (EO) are based on ImageNet or medium-size, labeled remote sensing(RS) datasets. We share an unlabeled RS dataset SSL4EO-S12 (Self-SupervisedLearning for Earth Observation - Sentinel-1/2) to assemble a large-scale,global, multimodal, and multi-seasonal corpus of satellite imagery from the ESASentinel-1 \& -2 satellite missions. For EO applications we demonstrateSSL4EO-S12 to succeed in self-supervised pre-training for a set of methods:MoCo-v2, DINO, MAE, and data2vec. Resulting models yield downstream performanceclose to, or surpassing accuracy measures of supervised learning. In addition,pre-training on SSL4EO-S12 excels compared to existing datasets. We make openlyavailable the dataset, related source code, and pre-trained models athttps://github.com/zhu-xlab/SSL4EO-S12.

SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation