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Archaeologists train a neural network to sort pottery fragments for them

Northern Arizona University
a Convolutional Neural Network
Journal of Archaeological Science
the Ars Orbital Transmission
CNMN Collection WIRED Media Group
Condé Nast

Kiona N. Smith
Chris Downum
Leszek Pawlowicz
Tusayan White Ware



the Kayenta Branch


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The New York Times
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In the US Southwest in particular, museums have collected sherds by the tens of thousands.Although all those broken bits may not look like much at first glance, they’re often the key to piecing together the past.“[Potsherds] provide archaeologists with critical information about the time a site was occupied, the cultural group with which it was associated, and other groups with whom they interacted,” said Northern Arizona University archaeologist Chris Downum, who co-authored a new study with Leszek Pawlowicz.Members of different cultures have always made their own container types, using their own techniques and decorating in their own ways. Even then, archaeologists don’t always agree on what’s what, which can impact how they tell the story of the past.Pawlowicz and Downum recently turned to machine-learning for a faster way to sort through all those mountains of potsherds.Between 825 and 1300 CE, people living in the canyons and mesas of northeast Arizona stored their food and water in hand-shaped containers that were elaborately decorated with dark brown or black geometric patterns on a white background. Their pottery, now called Tusayan White Ware, varied over time and between places, and archaeologists have sorted it further into a handful of smaller categories.That’s exactly what Pawlowicz and Downum asked four experienced archaeologists to do with 3,000 potsherd photos taken at museums in northeastern Arizona. That’s not an improvement over human accuracy, but it could offer a more efficient way to deal with the sheer number of potsherds some sites offer up.“This will free up time and effort for archaeologists to concentrate on the meaning of the results,” wrote Pawlowicz and Downum.Someday, the researchers suggest, a mobile or web application could connect archaeologists in the field or the lab to a CNN that could classify potsherd photos on the fly, link to similar sherds, and even offer metadata about the site.

As said here by Kiona N. Smith