Compared to what we see in the movies, archaeology is boring work. There are no mummies or action-packed shootouts against other “treasure hunters”. To be honest, there aren’t even that many treasures to hunt to begin with, unless you consider good ol’ pottery fragments as treasures. And yes, those are what archaeologists usually encounter on sites, and they collect thousands of these fragments and sort them out painstakingly. Tedious work, but at least no one dies, so I guess there’s a happy ending for everyone at the end of the day.
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Potsherds are ubiquitous at archaeological sites, and that’s true for pretty much every culture since people invented pottery. 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.
Thinking of a way to expedite the potsherd sorting process, Pawlowicz and Downum decided to turn to machine-learning.
For now, Pawlowicz and Downum’s recent study is a proof of concept. They chose a pottery type, Tusayan White Ware, that is especially easy for a computer to sort based on photos, because its patterns contrast so strongly with the background. A neural network would likely do reasonably well at sorting other types of decorated pottery, but so-called plainware—ceramics without any visible decoration or markings—would probably be a bridge too far.
Now that’s a brilliant idea.
Go over at Ars Technica to know more about this topic.
(Image Credit: Pawlowicz and Downum 2021/ Ars Technica)