ArticleJournal of the Royal Society, Interface2022
A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks.
Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Complexity begets simplicity: self-supervised learning for palaeontological images with few or no labels.Journal of the Royal Society, Interface · 2026Article
- Heterodonty in the teeth of the turiasaurian sauropod from Angeac-Charente (Early Cretaceous, southwestern France): Comparison of anatomical and geometric morphometric approaches.Journal of anatomy · 2026Article
- Deep learning enables automated detection of dinosaur footprints with high accuracy.Scientific reports · 2026Article
- Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- New frontiers in dinosaur exploration.Biology letters · 2025Review
- Article
- The macroecology of Mesozoic dinosaurs.Biology letters · 2024Review
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
- A nearly complete skull of the sauropod dinosaurRoyal Society open science · 2023Article
- Article
- Classification of large ornithopod dinosaur footprints using Xception transfer learning.PloS one · 2023Article
- A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks.Journal of the Royal Society, Interface · 2022Article
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Authors and funding
3 authors.
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Abstract
Fossil tracks are important palaeobiological data sources. The quantitative analysis of their shape, however, has been hampered by their high variability and lack of discrete margins and landmarks. We here present the first approach using deep convolutional neural networks (DCNNs) to study fossil tracks, overcoming the limitations of previous statistical approaches. We employ a DCNN to discriminate between theropod and ornithischian dinosaur tracks based on a total of 1372 outline silhouettes. The DCNN consistently outperformed human experts on an independent test set. We also used the DCNN to classify tracks of a large tridactyl trackmaker from Lark Quarry, Australia, the identity of which has been subject to intense debate. The presented approach can only be considered a first step towards the wider application of machine learning in fossil track research, which is not limited to classification problems. Current limitations, such as the subjectivity and information loss inherent in interpretive outlines, may be overcome in the future by training neural networks on three-dimensional models directly, though this will require an increased uptake in digitization among workers in the field.
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