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Economical crowdsourcing for camera trap image classification

Hsing, P.-Y.; Bradley, S.P.; Kent, V.T.; Hill, R.A.; Smith, G.C.; Whittingham, M.J.; Cokill, J.; Crawley, D.; Volunteers, MammalWeb; Stephens, P.A.

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Authors

P.-Y. Hsing

V.T. Kent

G.C. Smith

M.J. Whittingham

J. Cokill

D. Crawley

MammalWeb Volunteers



Abstract

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.

Citation

Hsing, P., Bradley, S., Kent, V., Hill, R., Smith, G., Whittingham, M., …Stephens, P. (2018). Economical crowdsourcing for camera trap image classification. Remote Sensing in Ecology and Conservation, 4(4), 361-374. https://doi.org/10.1002/rse2.84

Journal Article Type Article
Acceptance Date Apr 13, 2018
Online Publication Date Jul 4, 2018
Publication Date Dec 31, 2018
Deposit Date Apr 18, 2018
Publicly Available Date Mar 29, 2024
Journal Remote Sensing in Ecology and Conservation
Publisher Wiley Open Access
Peer Reviewed Peer Reviewed
Volume 4
Issue 4
Pages 361-374
DOI https://doi.org/10.1002/rse2.84

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Accepted Journal Article (1.2 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
© 2018 The Authors.Remote Sensing in Ecology and Conservationpublished by John Wiley & Sons Ltd on behalf of Zoological Society of London.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use,distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.







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