Self-Supervised Learning for Semantic Segmentation of Archaeological Monuments in DTMs
Deep learning models need a lot of labeled data to work well.In this study, we use a Self-Supervised Learning (SSL) method for semantic segmentation of archaeological monuments in Digital Terrain Models (DTMs).This method first uses unlabeled data to pretrain a model (pretext Blood Pressure Support task), and then fine-tunes it with a small labeled