Animal population censusing at scale with citizen science and photographic identification

Jason Parham, Jonathan Crall, Charles Stewart, Tanya Berger-Wolf, Daniel Ian Rubenstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations


Population censusing is critical to monitoring the health of an animal population. A census results in a population size estimate, which is a fundamental metric for deciding the demographic and conservation status of a species. Current methods for producing a population census are expensive, demanding, and may be invasive, leading to the use of overly-small sample sizes. In response, we propose to use volunteer citizen scientists to collect large numbers of photographs taken over large geographic areas, and to use computer vision algorithms to semi-automatically identify and count individual animals. Our data collection and processing are distributed, non-invasive, and require no specialized hardware and no scientific training. Our method also engages the community directly in conservation. We analyze the results of two population censusing events, the Great Zebra and Giraffe Count (2015) and the Great Grevy's Rally (2016), where combined we processed over 50,000 photographs taken with more than 200 different cameras and over 300 on-the-ground volunteers.

Original languageEnglish (US)
Title of host publicationSS-17-01
Subtitle of host publicationArtificial Intelligene for the Social Good; SS-17-02: Computational Construction Grammar and Natural Language Understanding; SS-17-03: Computational Context: Why It's Important, What It Means, and Can It Be Computed?; SS-17-04: Designing the User Experience of Machine Learning Systems; SS-17-05: Interactive Multisensory Object Perception for Embodied Agents; SS-17-06: Learning from Observation of Humans; SS-17-07: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence; SS-17-08: Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing
PublisherAI Access Foundation
Number of pages8
ISBN (Electronic)9781577357797
StatePublished - 2017
Event2017 AAAI Spring Symposium - Stanford, United States
Duration: Mar 27 2017Mar 29 2017

Publication series

NameAAAI Spring Symposium - Technical Report
VolumeSS-17-01 - SS-17-08


Other2017 AAAI Spring Symposium
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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