Interpretable Classification of Levantine Ceramic Thin Sections via Neural Networks

TL;DR

This paper demonstrates that deep learning models, specifically CNNs and ViTs, can effectively classify Levantine ceramic thin sections with high accuracy, offering an interpretable and efficient alternative to traditional petrographic analysis.

Contribution

The study introduces the application of transfer learning with CNNs and ViTs for ceramic classification, incorporating explainability techniques to interpret model decisions in archaeometric analysis.

Findings

ResNet18 achieved 92.11% accuracy

ViT reached 88.34% accuracy

Explainability methods reveal focus on mineralogical features

Abstract

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 Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), as complementary tools to support the classification of Levantine ceramics based on their petrographic fabrics. A dataset of 1,424 thin section images from 178 ceramic samples belonging to several archaeological sites across the Levantine area, mostly from the Bronze Age, with few samples dating to the Iron Age, was used to train and evaluate these models. The results demonstrate that transfer learning significantly improves classification performance, with a ResNet18 model achieving 92.11% accuracy and a ViT reaching 88.34%. Explainability techniques, including Guided Grad-CAM and attention maps, were applied to interpret and visualize the models' decisions, revealing that both CNNs and ViTs successfully focus on key mineralogical features for the classification of the samples into their respective petrographic fabrics. These findings highlight the potential of explainable AI in archaeometric studies, providing a reproducible and efficient methodology for ceramic analysis while maintaining transparency in model decision-making.