EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Deep Neural Networks for Hierarchical Taxonomic Fossil Classification of Carbonate Skeletal Grains

Sidhant Idgunji1, Madison Ho1, Jonathan L. Payne1, Daniel Lehrmann2, Michele Morsilli3, Khalid Al-Ramadan4, and Ardiansyah Koeshidayatullah1
Sidhant Idgunji et al.
  • 1Department of Geological Sciences, Stanford University, Stanford, CA 94305
  • 2Department of Geosciences, Trinity University, San Antonio, TX 78212
  • 3Department of Physics and Earth Science, University of Ferrara, Ferrara, Italy
  • 4Department of Geosciences, King Fahd University of Petroleum and Minerals, Saudi Arabia

The growing digitization of fossil images has vastly improved and broadened the potential application of big data and machine learning, particularly computer vision, in paleontology. Recent studies show that machine learning is capable of approaching human abilities of classifying images, and with the increase in computational power and visual data, it stands to reason that it can match human ability but at much greater efficiency in the near future. Here we demonstrate this potential of using deep learning to identify skeletal grains at different levels of the Linnaean taxonomic hierarchy. Our approach was two-pronged. First, we built a database of skeletal grain images spanning a wide range of animal phyla and classes and used this database to train the model. We used a Python-based method to automate image recognition and extraction from published sources. Second, we developed a deep learning algorithm that can attach multiple labels to a single image. Conventionally, deep learning is used to predict a single class from an image; here, we adopted a Branch Convolutional Neural Network (B-CNN) technique to classify multiple taxonomic levels for a single skeletal grain image. Using this method, we achieved over 90% accuracy for both the coarse, phylum-level recognition and the fine, class-level recognition across diverse skeletal grains (6 phyla and 15 classes). Furthermore, we found that image augmentation improves the overall accuracy. This tool has potential applications in geology ranging from biostratigraphy to paleo-bathymetry, paleoecology, and microfacies analysis. Further improvement of the algorithm and expansion of the training dataset will continue to narrow the efficiency gap between human expertise and machine learning.

How to cite: Idgunji, S., Ho, M., Payne, J. L., Lehrmann, D., Morsilli, M., Al-Ramadan, K., and Koeshidayatullah, A.: Deep Neural Networks for Hierarchical Taxonomic Fossil Classification of Carbonate Skeletal Grains, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16394,, 2021.


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