More than meets the eye: use of computer vision algorithms to identify stone tool material through the analysis of cut mark micro-morphology

Abstract

Artificial intelligence algorithms have recently been applied to taphonomic questions with great success, outperforming previous methods of bone surface modification (BSM) identification. Following these new developments, here we try different deep learning model architectures, optimizers and activation functions to assess if it is possible to identify a stone tool’s raw material simply by looking at the cut marks that it created on bone. The deep learning models correctly discerned between flint, sandstone and quartzite with accuracy rates as high as 78%. Also, single models seem to work better than ensemble ones, and there is no optimal combination of hyperparameters that perform better in every possible scenario. Model fine-tuning is thus advised as a protocol. These results consolidate the potential of deep learning methods to make classifications out of BSM’s microscopic features with a higher degree of confidence and more objectively than alternative taphonomic procedures.