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

Internet

https://account.journal.caa-international.org/index.php/up-j-jcaa/article/view/110

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