This post is all Dr. Flint Dibble’s fault. That fault is wholesale based on all the #realarchaeology posts that have been flying around the internet [1]. If you missed all that online content about archaeology, then consider diving into all that informationally rich real archeology content. This week Flint got me thinking about how artificial intelligence and machine learning fit within the broader academic domain of archaeology. Technology is always approaching the intersection with modernity. Our technology now is ultimately becoming very different based on what the fields of AI, ML, and robotics are able to accomplish. I should have thrown quantum computing on that list, but I’m still a little skeptical about scalability.
A lot of modern archaeology on television is about the discovery part of the process. Finding things like buried treasure, missing cities, or maybe a significant shipwreck yields pure excitement. Just this week I watched an episode of the ongoing television show Expedition Unknown with Josh Gates digging at Petra [2]. You may be aware that I’m generally interested in all things Indiana Jones related and this adventure certainly was. Josh Gates joined Dr. Pearce Paul Creasman onsite for the discovery and the American Center of Research is the group facilitating the actual archaeology.
Generally speaking, finding new things is hard, but the process of trying to understand them is where the work of archaeology happens. Applying some type of scientific rigor to the process of figuring things out brings forward quality and makes the process definable and repeatable. We have done a lot of exploring and studying the world with satellites every day. A lot of laboratory, office, or digging work happens that is more hands-on and is about the academic parts of archaeology. That is where I was curious about how both AI and ML fit into the actual practice of archaeology. I wondered what people are doing with advanced technology. I could easily imagine people trying a machine learning model to evaluate satellite images to try to find structures in a jungle or desert. You could use a machine learning model to match images of text fragments or match a partial text to other larger texts.
I’m going to share my top 10 thoughts about how AI or ML could be impactful within the field of archeology.
Automated Site Detection: Using AI to analyze satellite images and locate hidden archaeological sites
Predictive Modeling: Guided site discovery by predicting likely artifact locations from geological and historical data
Excavation Data Analysis: Speeding up artifact categorization and soil dating during digs
3D Reconstruction: Rebuilding artifacts or sites digitally to visualize original structures
Text Decipherment: Using AI to decode ancient texts and connect languages or symbols
Remote Sensing Interpretation: Processing LiDAR and radar data to reveal hidden structures
Artifact Classification: Identifying and classifying artifacts using computer vision
Preservation Monitoring: Predicting and preventing environmental damage to sites
Cross-dataset Analysis: Finding patterns across separate data sources for deeper historical connections
Virtual Archaeology and Immersive Experiences: AI can create virtual reality (VR) and augmented reality (AR) experiences, allowing researchers and the public to explore reconstructed sites and artifacts interactively
Beyond considering that list, you know I went out to Google Scholar and started to look for highly cited papers within this space [3]. I pulled together 5 papers you can read about AI and ML within the academic space of archaeology. None of these papers have very high citation numbers so they are not widely read like those papers I recently shared from Dr. Geoffrey Hinton that bridged 100,000 citations. These papers were in the sub 100 citation range and that does mean people are reading them, but not at the same prolific rates of core AI or ML papers.
Bickler, S. H. (2021). Machine learning arrives in archaeology. Advances in Archaeological Practice, 9(2), 186-191. https://www.researchgate.net/profile/Simon-Bickler/publication/351713328_Machine_Learning_Arrives_in_Archaeology/links/60a62e36a6fdcc731d3ea200/Machine-Learning-Arrives-in-Archaeology.pdf
Barceló, J. A. (2007). Automatic archaeology: Bridging the gap between virtual reality, artificial intelligence, and archaeology. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=9f03a88221022b93d07f271037776a2c01099fce
Mantovan, L., & Nanni, L. (2020). The computerization of archaeology: Survey on artificial intelligence techniques. SN Computer Science, 1(5), 267. https://arxiv.org/pdf/2005.02863
Casini, L., Marchetti, N., Montanucci, A., Orrù, V., & Roccetti, M. (2023). A human–AI collaboration workflow for archaeological sites detection. Scientific Reports, 13(1), 8699. https://www.nature.com/articles/s41598-023-36015-5
Argyrou, A., & Agapiou, A. (2022). A review of artificial intelligence and remote sensing for archaeological research. Remote Sensing, 14(23), 6000. https://www.mdpi.com/2072-4292/14/23/6000
The broader implications of how AI is reshaping archaeology’s future are currently unfolding. What we’re seeing now is that technology is actively transforming archaeology. As AI helps detect patterns and analyze data in ways humans simply can’t, it’s redefining the possibilities within this field. AI isn’t here to replace the archaeologist’s trowel but rather to enhance their insights. It’s an assistive tool, an enabler, expanding the reach and depth of what we can know about our past. And that’s exactly why this conversation about AI in archaeology matters—it’s the next step in understanding not just what we’ve found but, ultimately, what it means.
Footnotes:
[1] https://real-archaeology.com/
[2] https://www.cnn.com/2024/10/12/science/petra-tomb-indiana-jones-discovery/index.html
[3] https://scholar.google.com/scholar?hl=en&as_sdt=0%2C6&q=artificial+intelligence+archeology&btnG=
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