Reconstructing rock art chronology with transfer learning: A case study from Arnhem Land, Australia

Jarrad Kowlessar, James Keal, Daryl Wesley, Ian Moffat, Dudley Lawrence, Abraham Weson, Alfred Nayinggul, Mimal Land Management Aboriginal Corporation

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In recent years, machine learning approaches have been used to classify and extract style from media and have been used to reinforce known chronologies from classical art history. In this work we employ the first ever machine learning analysis of Australian rock art using a data efficient transfer learning approach to identify features suitable for distinguishing styles of rock art. These features are evaluated in a one-shot learning setting. Results demonstrate that known Arnhem Land Rock art styles can be resolved without knowledge of prior groupings. We then analyse the activation space of learned features and report on the relationships between styles and arrange these classes into a stylistic chronology based on distance within the activation space. By generating a stylistic chronology, it is shown that the model is sensitive to both temporal and spatial patterns in the distribution of rock art in the Arnhem Land Plateau region. More broadly, this approach is ideally suited to evaluating style within any material culture assemblage and overcomes the common constraint of small training data sets in archaeological machine learning studies.

Original languageEnglish
Pages (from-to)115-126
Number of pages12
JournalAustralian Archaeology
Issue number2
Early online dateMar 2021
Publication statusPublished - 2021


  • Arnhem Land
  • machine learning
  • Rock art
  • style


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