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The lack of quality labeled data is one of the main bottlenecks for trainingDeep Learning models. As the task increases in complexity, there is a higherpenalty for overfitting and unstable learning. The typical paradigm employedtoday is Self-Supervised learning, where the model attempts to learn from alarge corpus of unstructured and unlabeled data and then transfer thatknowledge to the required task. Some notable examples of self-supervision inother modalities are BERT for Large Language Models, Wav2Vec for SpeechRecognition, and the Masked AutoEncoder for Vision, which all utilizeTransformers to solve a masked prediction task. GeoAI is uniquely poised totake advantage of the self-supervised methodology due to the decades of datacollected, little of which is precisely and dependably annotated. Our goal isto extract building and road segmentations from Digital Elevation Models (DEM)that provide a detailed topography of the earths surface. The proposedarchitecture is the Masked Autoencoder pre-trained on ImageNet (with thelimitation that there is a large domain discrepancy between ImageNet and DEM)with an UperNet Head for decoding segmentations. We tested this model with 450and 50 training images only, utilizing roughly 5% and 0.5% of the original datarespectively. On the building segmentation task, this model obtains an 82.1%Intersection over Union (IoU) with 450 Images and 69.1% IoU with only 50images. On the more challenging road detection task the model obtains an 82.7%IoU with 450 images and 73.2% IoU with only 50 images. Any hand-labeled datasetmade today about the earths surface will be immediately obsolete due to theconstantly changing nature of the landscape. This motivates the clear necessityfor data-efficient learners that can be used for a wide variety of downstreamtasks.

Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks