• Corpus ID: 6055571

Wildbook: Crowdsourcing, computer vision, and data science for conservation

@article{BergerWolf2017WildbookCC,
  title={Wildbook: Crowdsourcing, computer vision, and data science for conservation},
  author={T. Berger-Wolf and Daniel I. Rubenstein and Charles V. Stewart and Jason A. Holmberg and Jason Remington Parham and S. Menon and Jonathan P. Crall and Jon Van Oast and Emre Kıcıman and Lucas N Joppa},
  journal={ArXiv},
  year={2017},
  volume={abs/1710.08880}
}
Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have… 
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References

SHOWING 1-10 OF 65 REFERENCES
Animal Population Censusing at Scale with Citizen Science and Photographic Identification
TLDR
This paper analyzes the results of two population censusing events, the Great Zebra and Giraffe Count and the Great Grevy’s Rally, where combined the authors processed over 50,000 photographs taken with more than 200 different cameras and over 300 on-the-ground volunteers.
Animal Population Estimation Using Flickr Images
While the technologies of the Information Age have produced staggering amounts of data about people, they are by and large failing the world’s wildlife. Even the simplest and most critical piece of…
Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
TLDR
This work deployed 225 camera traps across Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics and classified the images via the citizen-science website www.snapshotsereNGeti.org, yielding a final classification for each image and a measure of agreement among individual answers.
Animal Wildlife Population Estimation Using Social Media Images Collections
TLDR
A new framework is presented to take into account the social media bias when using this data source to provide wildlife population size estimates and it is shown that, surprisingly, this is a learnable and potentially solvable problem.
Unmanned Aerial Vehicles (UAVs) for Surveying Marine Fauna: A Dugong Case Study
TLDR
The results suggest that UAV systems may not be limited by sea state conditions in the same manner as sightings from manned surveys, and the overlap between images proved valuable for detecting animals that were masked by sun glitter in the corners of images, and identifying animals initially captured at awkward body angles.
A critique of density estimation from camera-trap data†
TLDR
This work critically review 47 published studies and discusses the problems associated with contemporary population estimates of elusive species from camera-trap data, and recommends greater transparency in study design and quality of the data and the recently developed spatially explicit capture–recapture models as an alternative approach.
Citizen Science as an Ecological Research Tool: Challenges and Benefits
Citizen science, the involvement of volunteers in research, has increased the scale of ecological field studies with continent-wide, centralized monitoring efforts and, more rarely, tapping of…
Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models
TLDR
It is investigated whether occupancy models can correct for the observation, reporting and detection biases in opportunistic data, enabling species trends to be monitored for species groups and regions where it is not feasible to collect standardized data on a large scale.
Calibrating a camera trap–based biased mark–recapture sampling design to survey the leopard population in the N'wanetsi concession, Kruger National Park, South Africa
Estimating large carnivore abundance can be challenging. A biased leopard (Panthera pardus) population survey was conducted in the N’wanetsi concession in the Kruger National Park (KNP), South…
Statistical solutions for error and bias in global citizen science datasets
...
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4
5
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