# Machine learning applications in archaeological practices: a review arXiv: https://arxiv.org/abs/2501.03840 ยท HTML: https://mlsanity.com/paper/2501.03840/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 287 references that are in the map. - First posted on arXiv 7 Jan 2025. ## Builds on (most cited first, 30 of 30) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014) - [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017) - ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016) - [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019) - [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023) - [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017) - [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016) - [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016) - [Neural General Circulation Models for Weather and Climate](https://mlsanity.com/paper/2311.07222/) (2023) - [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019) - [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021) - [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023) - [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022) - [Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models](https://mlsanity.com/paper/2102.02503/) (2021) - [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022) - [A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends](https://mlsanity.com/paper/2311.10372/) (2023) - [A Review on Deep Learning in UAV Remote Sensing](https://mlsanity.com/paper/2101.10861/) (2021) - [Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey](https://mlsanity.com/paper/2001.04074/) (2020) - [Generative Disco: Text-to-Video Generation for Music Visualization](https://mlsanity.com/paper/2304.08551/) (2023) - [Mapping the Ethics of Generative AI: A Comprehensive Scoping Review](https://mlsanity.com/paper/2402.08323/) (2024) - [Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines](https://mlsanity.com/paper/2312.05235/) (2023) - [70 years of machine learning in geoscience in review](https://mlsanity.com/paper/2006.13311/) (2020) - [Theory In, Theory Out: The uses of social theory in machine learning for social science](https://mlsanity.com/paper/2001.03203/) (2020) - [Classical Statistics and Statistical Learning in Imaging Neuroscience](https://mlsanity.com/paper/1603.01857/) (2016) - [Use and Misuse of Machine Learning in Anthropology](https://mlsanity.com/paper/2209.02811/) (2022) - [Stylistic Clusters and the Syrian/South Syrian Tradition of First-Millennium BCE Levantine Ivory Carving: A Machine Learning Approach](https://mlsanity.com/paper/1401.0871/) (2014) ## Built on by (most cited first, 2 of 2) - [OCR-Based Field Extraction for Archaeological Pottery Metadata: The CENTURIA Dataset](https://mlsanity.com/paper/2608.30616/) (2026) - [PyPottery: an AI-powered end-to-end suite for pottery processing and publication](https://mlsanity.com/paper/2610.02072/) (2026) ## Abstract (arXiv) 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 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 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 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.