Self-supervised pretraining bears the potential to generate expressive representations from large-scale Earth observation (EO) data without human annotation. However, most existing pretraining in the field is based on ImageNet or medium-sized, labeled remote sensing (RS) datasets. In this article, we share an unlabeled dataset Self-Supervised Learning for Earth Observation-Sentinel-1/2 ( SSL4EO - S12 ) to assemble a large-scale, global, multimodal, and multiseasonal corpus of satellite imagery. We demonstrate SSL4EO-S12 to succeed in self-supervised pretraining for a set of representative methods: momentum contrast (MoCo), self-distillation with no labels (DINO), masked autoencoders (MAE), and data2vec, and multiple downstream applications, including scene classification, semantic segmentation, and change detection. Our benchmark results prove the effectiveness of SSL4EO-S12 compared to existing datasets. The dataset, related source code, and pretrained models are available at https://github.com/zhu-xlab/SSL4EO-S12 .

中文翻译：

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SSL4EO-S12：用于地球观测中自我监督学习的大规模多模式、多时态数据集[软件和数据集]

自监督预训练具有从大规模地球观测（EO）数据生成表达表示的潜力，无需人工注释。然而，该领域现有的大多数预训练都是基于 ImageNet 或中等规模的标记遥感 (RS) 数据集。在本文中，我们分享一个未标记的数据集地球观测自我监督学习-Sentinel-1/2 ( SSL4EO- S12）组装大规模、全球、多模式、多季节的卫星图像语料库。我们证明 SSL4EO-S12 成功地对一组代表性方法进行了自监督预训练：动量对比 (MoCo)、无标签自蒸馏 (DINO)、掩码自动编码器 (MAE) 和 data2vec，以及多个下游应用程序，包括场景分类、语义分割和变化检测。与现有数据集相比，我们的基准测试结果证明了 SSL4EO-S12 的有效性。数据集、相关源代码和预训练模型可在以下位置获取：https://github.com/zhu-xlab/SSL4EO-S12 。