The Role of Citizen Science and Deep Learning in Camera Trapping

Matyáš Adam, Pavel Tomášek, Jiří Lehejček, Jakub Trojan and Tomáš Jůnek
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Matyáš Adam: Faculty of Logistics and Crisis Management, Tomas Bata University in Zlín, 686 01 Uherské Hradiště, Czech Republic
Pavel Tomášek: Faculty of Logistics and Crisis Management, Tomas Bata University in Zlín, 686 01 Uherské Hradiště, Czech Republic
Jiří Lehejček: Faculty of Logistics and Crisis Management, Tomas Bata University in Zlín, 686 01 Uherské Hradiště, Czech Republic
Jakub Trojan: Institute of Geonics, Department of Environmental Geography, The Czech Academy of Sciences, 602 00 Brno, Czech Republic
Tomáš Jůnek: Faculty of Environmental Sciences, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic

Sustainability, 2021, vol. 13, issue 18, 1-14

Abstract: Camera traps are increasingly one of the fundamental pillars of environmental monitoring and management. Even outside the scientific community, thousands of camera traps in the hands of citizens may offer valuable data on terrestrial vertebrate fauna, bycatch data in particular, when guided according to already employed standards. This provides a promising setting for Citizen Science initiatives. Here, we suggest a possible pathway for isolated observations to be aggregated into a single database that respects the existing standards (with a proposed extension). Our approach aims to show a new perspective and to update the recent progress in engaging the enthusiasm of citizen scientists and in including machine learning processes into image classification in camera trap research. This approach (combining machine learning and the input from citizen scientists) may significantly assist in streamlining the processing of camera trap data while simultaneously raising public environmental awareness. We have thus developed a conceptual framework and analytical concept for a web-based camera trap database, incorporating the above-mentioned aspects that respect a combination of the roles of experts’ and citizens’ evaluations, the way of training a neural network and adding a taxon complexity index. This initiative could well serve scientists and the general public, as well as assisting public authorities to efficiently set spatially and temporarily well-targeted conservation policies.

Keywords: artificial intelligence; crowdsourcing; environmental monitoring; conceptual framework; wildlife (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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