Machine learning applications in archaeological practices: a review

2025-01-07

AI Summary

Machine learning (ML) applications in archaeology suffer from technical misuse, ill-defined research objectives, methodological opacity, and a critical lack of unsupervised approaches. Method: We systematically review 135 peer-reviewed publications (1997–2022), quantifying task distributions and identifying methodological gaps; we then propose the first archaeology-specific ML methodology workflow, grounded in problem-driven design, data suitability assessment, and interdisciplinary collaboration. Contribution/Results: The analysis reveals that automated structural recognition and artifact classification dominate current practice, while clustering and other unsupervised tasks remain severely underutilized. We critically evaluate the applicability boundaries and reporting standards of artificial neural networks, ensemble methods, and supervised/unsupervised models in archaeological contexts. Results indicate explosive growth in ML adoption—especially post-2019—and establish foundational standards for reproducible, interpretable, and collaborative archaeological ML practice, addressing a longstanding methodological void.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Humans and AI: Human-in-the-loop Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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. 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. Automatic structure detection and artefact classification were the most represented tasks in the articles reviewed, followed by taphonomy, and archaeological predictive modelling. From the review, 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 is gaining in popularity it remains 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 proposed a workflow guide for archaeologists to develop coherent and consistent methodologies adapted to their research questions, project scale and data. As in many other areas, machine learning is rapidly becoming an important tool in archaeological research and practice, useful for the analyses of large and 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 archaeology.

Problem

• Machine Learning
• Archaeology
• Application Evaluation

Innovation

• Machine Learning in Archaeology
• Supervised Learning Methods
• Guiding Principles for Effective Application