Self-Supervised Learning for Semantic Segmentation of Archaeological Monuments in DTMs

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 task), and then fine-...

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Bibliographic Details
Main Authors:	Bashir Kazimi, Monika Sester
Format:	Article
Language:	English
Published:	Ubiquity Press 2023-11-01
Series:	Journal of Computer Applications in Archaeology
Subjects:	self-supervised learning digital terrain models deep learning archaeology convolutional neural networks generative adversarial networks relief visualization
Online Access:	https://account.journal.caa-international.org/index.php/up-j-jcaa/article/view/110

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