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Graphs and pictures showing how computer vision classifies pottery

Computer vision is probing the history of ancient pottery.

What’s new: Researchers at Northern Arizona University developed a machine learning model that identifies different styles of Native American painting on ceramic fragments and sorts the shards by historical period.

How it works: The researchers started with an ensemble of VGG16 and ResNet50 convolutional neural networks pretrained on ImageNet. They fine-tuned the ensemble to predict pottery fragments’ historical period.

  • The researchers collected 3064 photographs of pottery fragments from the southwestern U.S. Four experts labeled each photo as belonging to one of nine periods between 825 AD and 1300 AD. A majority of the experts had to agree on the type of pottery in each image for it to be included in the fine-tuning dataset, which contained 2,407 images.
  • To make their training data more robust, the researchers randomly rotated, shrunk, or enlarged every photo prior to each training cycle.
  • Heat maps generated using Grad-CAM highlighted the design features that were most influential in the model’s decisions.

Results: In tests, the model classified tens of thousands of unlabeled fragments. It scored higher than two experts and roughly equal to two others.

Behind the news: AI is helping archaeologists discover long-lost civilizations and make sense of clues they had already uncovered.

  • Researchers found evidence of ancient settlements by training a model to interpret lidar readings taken during flights over Madagascar and the U.S.
  • Using a similar method, archaeologists developed a network that identified underground tombs in aerial photography.
  • A model that reads cuneiform is helping scholars translate ancient Persian tablets.

Why it matters: For human archaeologists, learning to recognize the patterns on ancient pottery takes years of practice, and they often disagree on a given fragment’s provenance. Machine learning could sift through heaps of pottery shards far more quickly, allowing the humans to focus on interpreting the results.

We’re thinking: Even when experts correctly identify a fragment, they can’t always explain what features led them to their conclusion. Heat maps from machine learning models could help teach the next generation of archaeologists how to read the past.


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