Machine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a
range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are …

The significance of data acquisition in archaeological practice has consistently held great
importance. Over the past few decades, the growing prevalence of digitization in acquiring …

The integration of AI in archaeology poses several risks due to the oversimplification of
complex archaeological data for computational ease. This reductionist approach fosters a …

Rock art is globally recognized as significant, yet the resources allocated to the study and
exploration of this important form of cultural heritage are often scarce. In areas where …

This paper is the editorial of the virtual special issue (VSI)“Pattern recognition and artificial
intelligence techniques for cultural heritage”, of which the authors of this paper have been …

In recent years, machine learning approaches have been used to classify and extract style
from media and have been used to reinforce known chronologies from classical art history …

Due to environmental reasons, most of the artifacts are fragmented, and the surface
information of the artifacts is also blurred. The traditional method of repairing artifacts mainly …

Ceramic vessels are one of the most important witnesses of everyday life as they withstand
burial conditions well and are discarded when broken, while rarely recycled. They have the …

Today, it is widely accepted that typology is a biased and inconsistent attempt to classify
archaeological material based on the similarity of a predefined set of features. In this …

Inventories of a rock art site often do not include tracings due to time and budget constraints,
however they are still important for aiding interpretation. While digital inventories from 3D …