Article Information
History
- September 10, 2020.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Author Information
- 1Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Recanati-Kaplan Centre, Tubney, UK
- 2Department of Computer Science, University of Bath, Bath, UK
- 3Department of Engineering Science, University of Oxford, Oxford, UK
- 4Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
- ↵† Corresponding author; email: isla.duporge{at}zoo.ox.ac.uk
↵* Equal contribution
Posted September 10, 2020.
Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes
