A deep-learning model for predictive archaeology and archaeological community detection - Abraham Resler (Springer Nature, 2021)
Abstract Deep learning is a powerful tool for exploring large datasets and discovering new patterns. This work presents an account of a metric learning-based deep convolutional neural network (CNN) applied to an archaeological dataset. The proposed account speaks of three stages: training, testing/validating, and community detection. Several thousand artefact images, ranging from the Lower Palaeolithic period (1.4 million years ago) to the Late Islamic period (fourteenth century AD), were used to train the model (i.e., the CNN), to discern artefacts by site and period. After training, it attained a comparable accuracy to archaeologists in various periods. In order to test the model, it was called to identify new query images according to similarities with known (training) images. Validation blinding experiments showed that while archaeologists performed as well as the model within their field of expertise, they fell behind concerning other periods. Lastly, a community detection algorithm based on the confusion matrix data was used to discern affiliations across sites. A case-study on Levantine Natufian artefacts demonstrated the algorithm’s capacity to discern meaningful connections. As such, the model has the potential to reveal yet unknown patterns in archaeological data.