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How Archaeologists Are Using Deep Learning to Dig Deeper

  • November 26, 2020

That dream became the ArchAIDE project, a digital tool that will allow archaeologists to photograph a piece of pottery in the field and have it identified by convolutional neural networks. The project, which received financing from the European Union’s Horizon 2020 research and innovation program, now involves researchers from across Europe, as well as a team of computer scientists from Tel Aviv University in Israel who designed the C.N.N.s.

The project involved digitizing many of the paper catalogs and using them to train a neural network to recognize different types of pottery vessels. A second network was trained to recognize the profiles of pottery sherds. So far, ArchAIDE can identify only a few specific pottery types, but as more researchers add their collections to the database the number of types is expected to grow.

“I dream of a catalog of all types of ceramics,” Dr. Anichini said. “I don’t know if it is possible to complete in this lifetime.”

Saving time is one of the biggest advantages of using convolutional neural networks. In marine archaeology, ship time is expensive, and divers cannot spend too much time underwater without risking serious pressure-related injuries. Chris Clark, an engineer at Harvey Mudd College in Claremont, Calif., is addressing both problems by using an underwater robot to make sonar scans of the seafloor, then using a convolutional neural network to search the images for shipwrecks and other sites. In recent years he has been working with Timmy Gambin, an archaeologist at the University of Malta, to search the floor of the Mediterranean Sea around the island of Malta.

Their system got off to a rough start: On one of its first voyages, they ran their robot into a shipwreck and had to send a diver down to retrieve it. Things improved from there. In 2017, the network identified what turned out to be the wreck of a World War II-era dive bomber off the coast of Malta. Dr. Clark and Dr. Gambin are now working on another site that was identified by the network, but did not want to discuss the details until the research has gone through peer-review.

Article source: https://www.nytimes.com/2020/11/24/science/artificial-intelligence-archaeology-cnn.html

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