This work proposes a hybrid unsupervised and supervised learning method to pre-train models applied in Earth observation downstream tasks when only a handful of labels denoting very general semantic concepts are available. We combine a contrastive approach to pre-train models with a pixel-wise regression pre-text task to predict coarse elevation maps, which are commonly available worldwide. We hypothesize that this will allow the model to pre-learn useful representations, as there is generally some correlation between elevation maps and targets in many remote sensing tasks. We assess the performance of our approach on a binary semantic segmentation task and a binary image classification task, both derived from a dataset created for the northwest of Colombia. In both cases, we pre-train our models with 39k unlabeled images, fine-tune them on the downstream tasks with only 80 labeled images, and evaluate them with 2944 labeled images. Our experiments show that our methods, GLCNet+Elevation for segmentation, and SimCLR+Elevation for classification, outperform their counterparts without the pixel-wise regression pre-text task, namely SimCLR and GLCNet, in terms of macro-average F1 Score and Mean Intersection over Union (MIoU). Our study not only encourages the development of pre-training methods that leverage readily available geographical information, such as elevation data, to enhance the performance of self-supervised methods when applied to Earth observation tasks, but also promotes the use of datasets with high-level semantic labels, which are more likely to be updated frequently. Project code can be found in this link \href{https://github.com/omarcastano/Elevation-Aware-SSL}{https://github.com/omarcastano/Elevation-Aware-SSL}.

翻译：本研究提出一种混合无监督与监督学习方法，用于在仅具备少量表示通用语义概念的标签时预训练地球观测下游任务模型。我们将对比学习预训练方法与逐像素回归前置任务相结合，以预测全球广泛可用的粗略高程图。我们假设这能使模型预学到有用的表征，因为许多遥感任务中高程图与目标之间存在一定相关性。我们在源自哥伦比亚西北部数据集的二值语义分割任务和二值图像分类任务上评估了该方法性能。两种情况下，我们使用39,000张无标签图像预训练模型，仅用80张带标签图像在下游任务上进行微调，再用2,944张带标签图像进行评估。实验表明，我们的方法（分割任务采用GLCNet+Elevation，分类任务采用SimCLR+Elevation）在宏平均F1分数和平均交并比（MIoU）指标上优于未使用逐像素回归前置任务的对应方法（即SimCLR和GLCNet）。本研究不仅鼓励开发利用易获取地理信息（如高程数据）的预训练方法以增强自监督方法在地球观测任务中的性能，也推动使用具有高层语义标签的数据集（此类标签更可能频繁更新）。项目代码可访问链接 \href{https://github.com/omarcastano/Elevation-Aware-SSL}{https://github.com/omarcastano/Elevation-Aware-SSL}。