  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}.
