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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Economical crowdsourcing for camera trap image classification
Remote Sensing in Ecology and Conservation, Volume 4, No. 4, Year 2018
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Description
Camera trapping is widely used to monitor mammalian wildlife but creates large image datasets that must be classified. In response, there is a trend towards crowdsourcing image classification. For high-profile studies of charismatic faunas, many classifications can be obtained per image, enabling consensus assessments of the image contents. For more local-scale or less charismatic communities, however, demand may outstrip the supply of crowdsourced classifications. Here, we consider MammalWeb, a local-scale project in North East England, which involves citizen scientists in both the capture and classification of sequences of camera trap images. We show that, for our global pool of image sequences, the probability of correct classification exceeds 99% with about nine concordant crowdsourced classifications per sequence. However, there is high variation among species. For highly recognizable species, species-specific consensus algorithms could be even more efficient; for difficult to spot or easily confused taxa, expert classifications might be preferable. We show that two types of incorrect classifications – misidentification of species and overlooking the presence of animals – have different impacts on the confidence of consensus classifications, depending on the true species pictured. Our results have implications for data capture and classification in increasingly numerous, local-scale citizen science projects. The species-specific nature of our findings suggests that the performance of crowdsourcing projects is likely to be highly sensitive to the local fauna and context. The generality of consensus algorithms will, thus, be an important consideration for ecologists interested in harnessing the power of the crowd to assist with camera trapping studies. © 2018 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
Authors & Co-Authors
Hsing, Pen Yuan
United Kingdom, Durham
Durham University
Kent, Vivien T.
United Kingdom, Houghton
Durham Wildlife Trust
Hill, Russell A.
Unknown Affiliation
Whittingham, Mark J.
United Kingdom, Newcastle
Newcastle University
Stephens, Philip A.
United Kingdom, Durham
Durham University
Statistics
Citations: 35
Authors: 5
Affiliations: 4
Identifiers
Doi:
10.1002/rse2.84
ISSN:
20563485