Nature Communications (Oct 2020)
Deep learning-assisted comparative analysis of animal trajectories with DeepHL
- Takuya Maekawa,
- Kazuya Ohara,
- Yizhe Zhang,
- Matasaburo Fukutomi,
- Sakiko Matsumoto,
- Kentarou Matsumura,
- Hisashi Shidara,
- Shuhei J. Yamazaki,
- Ryusuke Fujisawa,
- Kaoru Ide,
- Naohisa Nagaya,
- Koji Yamazaki,
- Shinsuke Koike,
- Takahisa Miyatake,
- Koutarou D. Kimura,
- Hiroto Ogawa,
- Susumu Takahashi,
- Ken Yoda
Affiliations
- Takuya Maekawa
- Graduate School of Information Science and Technology, Osaka University
- Kazuya Ohara
- Graduate School of Information Science and Technology, Osaka University
- Yizhe Zhang
- Graduate School of Information Science and Technology, Osaka University
- Matasaburo Fukutomi
- Graduate School of Life Science, Hokkaido University
- Sakiko Matsumoto
- Graduate School of Environmental Studies, Nagoya University
- Kentarou Matsumura
- Graduate School of Environmental and Life Science, Okayama University
- Hisashi Shidara
- Department of Biological Sciences, Hokkaido University
- Shuhei J. Yamazaki
- Graduate School of Science, Osaka University
- Ryusuke Fujisawa
- Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology
- Kaoru Ide
- Graduate School of Brain Science, Doshisha University
- Naohisa Nagaya
- Department of Intelligent Systems, Kyoto Sangyo University
- Koji Yamazaki
- Department of Forest Science, Tokyo University of Agriculture
- Shinsuke Koike
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology
- Takahisa Miyatake
- Graduate School of Environmental and Life Science, Okayama University
- Koutarou D. Kimura
- Graduate School of Science, Osaka University
- Hiroto Ogawa
- Department of Biological Sciences, Hokkaido University
- Susumu Takahashi
- Graduate School of Brain Science, Doshisha University
- Ken Yoda
- Graduate School of Environmental Studies, Nagoya University
- DOI
- https://doi.org/10.1038/s41467-020-19105-0
- Journal volume & issue
-
Vol. 11,
no. 1
pp. 1 – 15
Abstract
Comparative analysis of animal behaviour using locomotion data such as GPS data is difficult because the large amount of data makes it difficult to contrast group differences. Here the authors apply deep learning to detect and highlight trajectories characteristic of a group across scales of millimetres to hundreds of kilometres.
