A deep variational convolutional Autoencoder for unsupervised features extraction of ceramic profiles. A case study from central Italy

Abstract

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 and a huge amount of data has accumulated over time. This data can be handled by Machine Learning algorithms which are rapidly gaining popularity in archaeology. Although most approaches can be referred to classification tasks, in this contribution a particular type of Neural Network is proposed for feature extraction from archaeological ceramics. Through this proposed method it is possible to gain a numerical weighted representation of the pottery profile that can be used for multivariate analyses. The case study will focus on regionalisation processes in pottery production between the end of the 2nd millennium BC and the first half of the 1st millennium BC in central Tyrrhenian Italy, a period that saw major transformations in the cultural and socio-political structure of Ancient Italy. The results seem to confirm the regionalisation hypothesis and offer interesting insights into the quantitative study of archaeological ceramics.