The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms. Indeed, raw image data are abundant while labels are scarce, therefore it is crucial to leverage unlabeled inputs to build better models. The availability of large databases have been key for the development of learning algorithms with high level performance. Despite the major role of machine learning in Earth Observation to derive products such as land cover maps, datasets in the field are still limited, either because of modest surface coverage, lack of variety of scenes or restricted classes to identify. We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images, accounting for more than 200 billions samples (pixels); it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. Nevertheless, the most distinctive quality of MiniFrance is being the only dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario. Along with this dataset, we present tools for data representativeness analysis in terms of appearance similarity and a thorough study of MiniFrance data, demonstrating that it is suitable for learning and generalizes well in a semi-supervised setting. Finally, we present semi-supervised deep architectures based on multi-task learning and the first experiments on MiniFrance. These results will serve as baselines for future work on semi-supervised learning over the MiniFrance dataset. The Minifrance suite and related semi-supervised networks will be publicly available to promote semi-supervised works in Earth Observation.

半监督学习技术的发展对于增强机器学习算法的泛化能力至关重要。实际上，原始图像数据很丰富，而标签却很少，因此利用未标记的输入来构建更好的模型至关重要。大型数据库的可用性对于开发具有高性能的学习算法至关重要。尽管机器学习在获取地球观测产品（如土地覆盖图）方面发挥着重要作用，但由于表面覆盖范围适中，缺乏各种场景或无法识别的类别，该领域的数据集仍然受到限制。我们在MiniFrance套件“地球观测”中引入了一种用于半监督语义分割的新型大规模数据集。MiniFrance具有几项前所未有的属性：大规模，包含超过2000个超高分辨率的航空图像，占2000亿个样本（像素）以上；它是多种多样的，涵盖了法国的16个城市，气候，气候，景观以及城市和乡村场景都不同。考虑具有高层次语义的土地利用类别是具有挑战性的。尽管如此，MiniFrance最有特色的质量是该领域中专门为半监督学习而设计的唯一数据集：它的训练分区中包含带标签的图像和未带标签的图像，从而再现了逼真的场景。与该数据集一起，我们提供了外观相似性方面的数据代表性分析工具以及对MiniFrance数据的透彻研究，表明它适合于学习并且在半监督的情况下能够很好地概括。最后，我们介绍了基于多任务学习的半监督深度架构以及MiniFrance上的首次实验。这些结果将作为将来在MiniFrance数据集上进行半监督学习的基础。Minifrance套件和相关的半监督网络将公开可用，以促进“地球观测”中的半监督工作。