Interpretable classification of Levantine ceramic thin sections via neural networks

Interpretable classification of Levantine ceramic thin sections via neural networks

Classification of ceramic thin sections is fundamental for understanding ancient pottery production techniques, provenance, and trade networks. Although effective, traditional petrographic analysis is time-consuming. This study explores the application of deep learning models, specifically convoluti...

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Bibliographic Details
Main Authors: Sara Capriotti, Alessio Devoto, Simone Scardapane, Silvano Mignardi, Laura Medeghini
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
Levantine ceramics
materials science
deep learning
explainable AI
Online Access:https://doi.org/10.1088/2632-2153/ade6c4
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https://doi.org/10.1088/2632-2153/ade6c4

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