Machine learning (ML) techniques present new ways of approaching archaeological research questions and interest in applying these methods continues to grow. This repository documents the application of ML techniques to archaeological data, aiming to assist those working in the field by:
- providing an overview of the ways ML is being applied in archaeology
- sparking new ideas whilst reducing duplication of work
- encouraging the sharing of code, data, and other resources
- making resources more FAIR (Findable, Accessible, Interoperable, and Reuseable)
By doing this, we hope to support practitioners to learn about, critically apply, or contribute to conversations about, machine learning techniques for archaeology.
This project is open for contributions!
Check out our πΊοΈ roadmap to get an overview of the current milestones we're working towards and find out how to participate.
Archived releases of this repository with a citeable DOI will be made at regular intervals.
This project was inspired by the satellite-image-deep-learning and AncientMetagenomeDir projects, and was kicked off as part of Open Seeds cohort 8.
The repository is structured into sections by application area (e.g. remote sensing), and then by machine learning technique, with linked examples illustrating the uses of each technique. Use the contents list below β¬οΈ to browse the application areas included so far and jump to specific sections, or scroll down to view everything.
title | authors | year | data type | technique | paper | code | data |
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Using CarcassonNet to automatically detect and trace hollow roads in LiDAR data from the Netherlands | Verschoof-van der Vaart and Landauer | 2021 | lidar | classification | paper | code:tbc | data:tbc |
title | authors | year | data type | technique | paper | code | data |
---|---|---|---|---|---|---|---|
Investigating ancient agricultural field systems in Sweden from airborne LIDAR data by using convolutional neural network | Küçükdemirci et al | 2022 | lidar | segmentation | paper | code:tbc | data:tbc |
title | authors | year | type | technique | language | paper | model |
---|---|---|---|---|---|---|---|
ArchaeoBERT | Alex Brandsen | 2023 | BERT | Masked Language Modelling | English | paper | model |
ArchaeoBERT-NER | Alex Brandsen | 2023 | BERT | Named Entity Recognition | English | paper | model |
ArcheoBERTje | Alex Brandsen | 2023 | BERT | Masked Language Modelling | Dutch | paper | model |
ArcheoBERTje-NER | Alex Brandsen | 2023 | BERT | Named Entity Recognition | Dutch | paper | model |
bert-base-german-cased-archaeo | Alex Brandsen | 2023 | BERT | Masked Language Modelling | German | paper | model |
bert-base-german-cased-archaeo-NER | Alex Brandsen | 2023 | BERT | Named Entity Recognition | German | paper | model |
title | authors | year | type | format | language | paper | data |
---|---|---|---|---|---|---|---|
Creating a Dataset for Named Entity Recognition in the Archaeology Domain | Brandsen et al. | 2020 | data | CoNNL | Dutch | paper | data |
title | authors | year | data type | technique | paper | code | data |
---|---|---|---|---|---|---|---|
A supervised machine-learning approach towards geochemical predictive modelling in archaeology | Oonk and Spijker | 2015 | soil geochemistry | k-nearest neighbours, suppert vector machines, and artificial neural networks | paper | code:tbc | data:tbc |
Mapping Tasmania's cultural landscapes: Using habitat suitability modelling of archaeological sites as a landscape history tool | Jones et al. | 2019 | climate and topography | Random Forest | paper | code:tbc | data:tbc |
Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument | Yaworsky et al. | 2020 | climate, topography, and environmental productivity | Random Forest | paper | code | data |
An Explorative Application of Random Forest Algorithm for Archaeological Predictive Modeling. A Swiss Case Study | Castiello and Tonini | 2021 | topography and soil characteristics | Random Forest | paper | code:tbc | data:tbc |
title | authors | year | data type | technique | paper | code | data |
---|---|---|---|---|---|---|---|
A bioavailable strontium isoscape for Western Europe: A machine learning approach | Bataille et al. | 2018 | strontium | Random Forest | paper | code | data:tbc |
Advances in global bioavailable strontium isoscapes | Bataille et al. | 2020 | strontium | Random Forest | paper | code | data |
A bio-available strontium isoscape for eastern Beringia: a tool for tracking landscape use of Pleistocene megafauna | Funck et al. | 2020 | strontium | Random Forest | paper | code:tbc | data |