Title
Camera Trap Images used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science"
Published Date
2018-08-28
Authors
Group
University of Minnesota - School of Physics and Astronomy
University of Minnesota - Data Science MS program
Author Contact
Willi, Marco (will5448@umn.edu)
Type
Dataset
Field Study Data
Observational Data
Abstract
This dataset provides the camera trap images used in "Identifying Animal Species in Camera Trap Images using Deep
Learning and Citizen Science" as well as meta-data about the images. The Snapshop Serengeti collection includes 6,163,870 images in JPG format. The Snapshot Wisconsin collection includes 497,204 images in JPG format. The Camera CATalogue collection include 506,241 images in JPG format. Excluded are the images for the dataset "Elephant Expedition" which will be published separately outside DRUM. Also excluded are images of humans due to privacy reasons.
Description
All images were downloaded from Zooniverse and have been resized to 330x330 pixels.
Funding information
Sponsorship:
This study was partially supported by the NSF under award IIS 1619177; The development of the Zooniverse platform was partially supported by a Global Impact Award from Google.; We also acknowledge support from STFC under grant ST/N003179/1.; EE was funded by the University of Oxford’s Hertford College Mortimer May fund.
Referenced by
Willi M, Pitman R, Cardoso A, Locke C, Swanson A, Boyer A, Veldthuis M, Fortson L, Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science, 2018, submitted to Methods in Ecology and Evolution
Related to
Software, Data & Models used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science"
License
CC0 1.0 Universal Public Domain Dedication
Suggested Citation
Willi, Marco; Pitman, Ross T; Cardoso, Anabelle W; Locke, Christina; Swanson, Alexandra; Boyer, Amy; Veldthuis, Marten; Fortson, Lucy.
(2018). Camera Trap Images used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science".
Retrieved from the Data Repository for the University of Minnesota,
https://doi.org/10.13020/D6T11K.
