Understanding and classifying lithic use wear: a systematic study using controlled tribological experiments and computer vision

The history of technology is crucial to understanding what makes us human. Microscopic wear traces present on the earliest (stone) tools excavated by archaeologists, offer key insights into how ancient people worked a variety of materials, like wood, skins, ivory, etc. Despite the enormous interpretive potential, archaeologists have experienced problems related to the difficulty and efficiency of securely identifying worked materials from wear traces, as well as reproducing each other’s results. This project will harness the power of tribology and artificial intelligence (AI) to advance understanding how wear patterns form and, at the same time, facilitate and democratize the identification process. Beside scientific publications, the investigators will produce an open-source tool for helping other researchers use their algorithms in their own work. The produced results will thus be useful to both the bio-engineering community as well as to archaeologists. Last but not least, to address pipeline issues in archaeological science, the project will increase diversity and the representation of minority groups by engaging high school students from New York City in the lab experiments.