Enhancing Ceramic Petrography through Deep Learning

Summary

This is an abstract from the "Ceramic Petrographers in the Americas: Recent Research and Methodological Advances" session, at the 89th annual meeting of the Society for American Archaeology.

Clay recipes reveal information about the local geology and the inclusion of different additives that make up a vessel, which in turn reflects the social, environmental, and technological context of ceramic manufacture. Ceramic petrography has long been instrumental in shedding light on key manufacturing techniques, identifying unique mineralogical signatures, and assessing patterns of cultural exchange among diverse communities. However, traditional methods used to characterize and quantify petrographic thin sections can be labor-intensive and time-consuming, making it difficult to meet the increasing demand for large comparative datasets. Here, we present recently developed machine-learning algorithms to enhance the efficiency, accuracy, and accessibility of these intricate analyses. We review the basic steps for conducting these approaches and illustrate their practical application for being incorporated into a petrographer’s standard toolkit. Streamlining petrographic workflows through machine-learning techniques will open new avenues for the quantitative and qualitative assessment of ceramic technology and its broader implications for past societies.

This Resource is Part of the Following Collections

• Ceramic Petrographers in the Americas: Recent Research and Methodological Advances   •
• Society for American Archaeology 89th Annual Meeting, New Orleans, LA (2024)

Keywords

Record Identifiers

Abstract Id(s): 40419.0