Archaeologists Use Machine Learning to Classify 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.