Classification of pottery assemblages in archaeology: a machine learning approach

Artefact classification is one of the main themes and an important practice since the beginnings of archaeology, while machine learning (ML) became one of the most efficient approaches to increase our knowledge in a number of disciplines. This thesis describes a ML model developed for the classification of pottery assemblages, identifying its benefits and limitations, focusing on the importance of artefacts features for the identification of vessel shape classes, and to what extent this kind of knowledge can be used to replicate classifications made by experts. The research also analyses different classes structures based on the ML model. The research dataset was based on an assemblage of pottery vessels representing nine shape classes and four archaeological sites from the Bronze Age Northeastern Syria, made available by the Arcane project. The classification methodology was based on principles of quantitative archaeology, using vessel measurements and categorical features,...Show moreArtefact classification is one of the main themes and an important practice since the beginnings of archaeology, while machine learning (ML) became one of the most efficient approaches to increase our knowledge in a number of disciplines. This thesis describes a ML model developed for the classification of pottery assemblages, identifying its benefits and limitations, focusing on the importance of artefacts features for the identification of vessel shape classes, and to what extent this kind of knowledge can be used to replicate classifications made by experts. The research also analyses different classes structures based on the ML model. The research dataset was based on an assemblage of pottery vessels representing nine shape classes and four archaeological sites from the Bronze Age Northeastern Syria, made available by the Arcane project. The classification methodology was based on principles of quantitative archaeology, using vessel measurements and categorical features, implemented by supervised and unsupervised learning ML algorithms and supporting methods from the scikit-learn and SciPy libraries. The Anaconda platform, the Jupyter notebook environment and ImageJ for image processing complete the main software used through the research. The research results indicate benefits and limitations in the application of ML models in the classification of pottery assemblages. The limitations are especially related to number of samples versus target classes, the homogeneity of the vessels context in the dataset, and the quality of data available for the samples. The results suggest that a ML model can be useful to experts, assisting in the identification of the most relevant artefact features and similarities among classes of artefacts, as well possible misclassifications, ultimately providing new insights into the classification of pottery assemblages in archaeology.Show less

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Classification of pottery assemblages in archaeology: a machine learning approach