New ornithopod footprints from the Lower Cretaceous Kitadani Formation, Fukui, Japan: Ichnotaxonomical implications

Tsukiji, Yuta; Azuma, Yoichi; Shiraishi, Fumito; Shibata, Masateru; Noda, Yoshikazu

doi:10.1016/j.cretres.2017.12.011

Cited by 12 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following the nomenclature proposed by Díaz-Martínez et al [ 3 ], we collected 274 dinosaur footprint illustrations from the literature: 111 Caririchnium [ 8 – 10 , 21 , 24 , 29 , 41 – 44 ], 36 Hadrosauropodus [ 2 , 14 , 41 , 45 – 50 ], and 127 Iguanodontipus [ 13 , 51 – 53 ]. The geographical distributions of these footprints are listed in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

“…To circumvent such issues, 3D techniques such as photogrammetry and light detection and ranging (LiDAR) have recently been introduced for digitizing and recording dinosaur footprints [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Furthermore, Lallensack [27] produced a program to create objective track outlines from 3D footprints in R environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of large ornithopod dinosaur footprints using Xception transfer learning

Ha,

Kim

2023

PLoS ONE

View full text Add to dashboard Cite

Large ornithopod dinosaur footprints have been confirmed on all continents except Antarctica since the 19th century. However, oversplitting problems in ichnotaxa have historically been observed in these footprints. To address these issues and distinguish between validated ichnotaxa, this study employed convolutional neural network-based Xception transfer learning to automatically classify ornithopod dinosaur tracks. The machine learning model was trained for 162 epochs (i.e., the number of full cycles of all training data through the model) using 274 data images, excluding horizontally flipped images. The trained model accuracy was 96.36%, and the validation accuracy was 92.59%. We demonstrate the performance of the machine learning model using footprint illustrations that are not included in the training dataset. These results show that the machine learning model developed in this study can properly classify footprint illustration data for large ornithopod dinosaurs. However, the quality of footprint illustration data (or images) inherently affects the performance of our machine learning model, which performs better on well-preserved footprints. In addition, because the developed machine-learning model is a typical supervised learning model, it is not possible to introduce a new label or class. Although this study used illustrations rather than photos or 3D data, it is the first application of machine-learning techniques at the academic level for verifying the ichnotaxonic assignments of large ornithopod dinosaur footprints. Furthermore, the machine learning model will likely aid researchers to classify the large ornithopod dinosaur footprint ichnotaxa, thereby safeguarding against the oversplitting problem.

show abstract