Automatic ceramic identification using machine learning. Lusitanian amphorae and Faience. Two Portuguese case studies

This article presents a novel approach to classifying archaeological artefacts using machine learning, specifically deep learning, rather than relying on traditional, time-consuming human-based methods. By employing Convolutional Neural Networks (CNNs), this approach aims to expedite and enhance the...

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Bibliographic Details
Main Authors:	Joel Santos, Diogo A.P. Nunes, Ruslan Padnevych, José Carlos Quaresma, Martim Lopes, Joana Gil, João Pedro Bernardes, Tania Manuel Casimiro
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2024-12-01
Series:	Science and Technology of Archaeological Research
Subjects:	Deep learning machine learning artificial intelligence Amphorae Faience ceramic identification
Online Access:	https://www.tandfonline.com/doi/10.1080/20548923.2024.2343214
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Automatic ceramic identification using machine learning. Lusitanian amphorae and Faience. Two Portuguese case studies

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