Code and data sharing are crucial practices to advance toward sustainable archaeology.
This article explores the performance of supervised machine learning classification methods …

Machine learning algorithms have revolutionized data analysis by uncovering hidden
patterns and structures. Clustering algorithms play a crucial role in organizing data into …

The need for a quantitative approach to the morphologic study of ceramics is becoming
increasingly evident. Ceramics are the most common material in many archaeological sites …

Organizing archaeological artefacts under a conceptual system is part and parcel of
archaeological research. As an abundant material category, pottery artefacts classified in an …

The so-called “axe-monies”, trapezoidal sheet metal objects made of arsenical copper alloy,
are associated with graves and hoards of the Manteño-Huancavilca (AD 600–1532) and …

Cuneiform tablets, emerging in ancient Mesopotamia around the late fourth millennium BCE,
represent one of humanity's earliest writing systems. Characterized by wedge-shaped marks …

Automated age estimation of archaeological artifacts is crucial for categorization and dating,
yet challenging due to variations in characteristics, degradation, and limited chronological …

Figure 1. View of the Temple of Apollo (6th century BCE), Ancient Corinth,
Greece............................................................................................................... x Figure 2. View …

This article presents a novel approach to classifying archaeological artefacts using machine
learning, specifically deep learning, rather than relying on traditional, time-consuming …

The study of material culture, particularly ceramics, is an important aspect of archaeology.
The classification and typology of ceramics is often a key focus, with traditional methods …