Abstract
Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods: from prehistory to modern times. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine learning in archaeology have only focused only on specific subfields of archaeology. Our review examined an exhaustive corpus of 135 articles published between 1997 and 2022. We observed a significant increase in the number of relevant publications from 2019 onwards, mainly concentrated in a few journals and mainly published in open-access format. Automatic structure detection and artefact classification were the most represented tasks in the articles reviewed, followed by taphonomy, archaeological predictive modelling, and architectural classification or reconstruction. From the corpus of articles analysed, clustering and unsupervised methods were underrepresented compared to supervised models. Artificial neural networks and ensemble learning account for two thirds of the total number of models used. However, if machine learning models are gaining in popularity, they remain subject to misunderstanding. We observed, in some cases, poorly defined requirements and caveats of the machine learning methods used. Furthermore, the goals and the needs of machine learning applications for archaeological purposes are in some cases unclear or poorly expressed. To address this, we propose here a workflow guide for archaeologists to develop coherent and consistent methodologies adapted to their research questions, project scale and available data. As in many areas of modern life, machine learning is rapidly becoming an important tool in archaeological research and practice, particularly useful for the analyses of large and highly multivariate data, although not without limitations. This review highlights the importance of well-defined and well-reported structured methodologies and collaborative practices to maximise the potential of applications of machine learning methods in archaeological research.