A Hybrid Geometric Morphometric Deep Learning Approach for Cut and Trampling Mark Classification

Featured Application Cut mark identification and analysis is a fundamental component for archaeological investigation. Cut mark analysis, however, has been the root of great debates, with some authors claiming to have the oldest cut marks in or outside of Africa. If these marks were to truly be anthropic in nature, then the repercussions of these findings would produce a paradigm shift for our understanding of human evolution. Unfortunately, the majority of methods available for cut mark classification are namely qualitative in nature. Here we provide a new, highly powerful artificially intelligent neural network classification model that can be used to quantitatively and more objectively overcome these issues, using 3D digital microscopy, Deep Learning and Geometric Morphometrics to obtain up to 100% accuracy in some cases. Abstract The concept of equifinality is currently one of the largest issues in taphonomy, frequently leading analysts to erroneously interpret the formation and functionality of archaeological and paleontological sites. An example of this equifinality can be found in the differentiation between anthropic cut marks and other traces on bone produced by natural agents, such as that of sedimentary abrasion and trampling. These issues are a key component in the understanding of early human evolution, yet frequently rely on qualitative features for their identification. Unfortunately, qualitative data is commonly susceptible to subjectivity, producing insecurity in research through analyst experience. The present study intends to confront these issues through a hybrid methodological approach. Here, we combine Geometric Morphometric data, 3D digital microscopy, and Deep Learning Neural Networks to provide a means of empirically classifying taphonomic traces on bone. Results obtained are able to reach over 95% classification, providing a possible means of overcoming taphonomic equifinality in the archaeological and paleontological register.