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AI Study and DinoTracker App Bring Data-Driven IDs to Dinosaur Footprints

The unsupervised model learns from thousands of footprint outlines to classify shapes with near expert-level agreement.

Overview

  • Researchers at Helmholtz-Zentrum Berlin and the University of Edinburgh report their method in PNAS and release the DinoTracker app for public use.
  • The network was trained on nearly 2,000 real tracks plus millions of augmented variants and discovered eight key axes of variation, including toe spread and heel position.
  • Only footprint outlines were input during training, reducing human-labeling bias and yielding about 80–93% concordance with expert classifications.
  • Model outputs highlight hypotheses that some tracks over 200 million years old show bird-like features and that Isle of Skye prints cluster with early ornithopods, pending further evidence.
  • DinoTracker is available via GitHub, enabling users to upload photos or sketches of tracks for instant similarity analysis to support research and citizen science.