Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks.

Abstract

Stone artifacts are often the most abundant class of objects found in archaeological sites but their consistent identification is limited by the number of experienced analysts available. We report a machine learning based technology for stone artifact identification as part of a solution to the lack of such experts directed at distinguishing worked stone objects from naturally occurring lithic clasts. Three case study locations from Egypt, Australia, and New Zealand provide a data set of 6769 2D images, 3868 flaked artifact and 2901 rock images used to train and test a machine learning model based on an openly available PyTorch implementation of Faster R-CNN ResNet 50. Results indicate 100% agreement between the model and original human derived classifications, a better performance than the results achieved independently by two human analysts who reassessed the 2D images available to the machine learning model. Machine learning neural networks provide the potential to consistently assess the composition of large archaeological assemblages composed of objects modified in a variety of ways.