The Role of Citizen Science and Deep Learning in Camera Trapping

Pavel Tomášek,Jiří Lehejček,Matyáš Adam,Tomáš Jůnek,Jakub Trojan

doi:10.3390/su131810287

Pavel Tomášek, Jiří Lehejček + Show 3 more

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https://doi.org/10.3390/su131810287

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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.

Highlights

In recent decades, there have been advances in low-impact methods for wildlife monitoring, such as natural markings [1], identification by footprints [2], vocal individuality [3], DNA sampling [4], and UAV monitoring [5], among others
One of athe aims of this to define the conceptual framework for represents photo/video fromcommunication a camera trap) hasismultiple associations with other entities possible further realisation of the system
With regards to its universal use and the optimising of efficiency of image classification, our logical concept of camera trap database (CTD) has combined involving citizen scientists and machine learning processes, enabling the filtering of blank images and automatic animal identification to be integrated into the data workflow

Summary

Introduction

There have been advances in low-impact methods for wildlife monitoring, such as natural markings [1], identification by footprints [2], vocal individuality [3], DNA sampling [4], and UAV monitoring [5], among others. One of the most frequently applied tools is the digital camera trap (CT) equipped with passive infrared (PIR) sensors [6]. A camera trap provides researchers with outputs, shedding light on a wide range of wildlife-related parameters, from species distribution to tempo-spatial behaviour [7,8]. CT can provide data on the effectiveness of conservation interventions and management efforts [9]. CT has made ecological monitoring more efficient in almost every kind of environmental condition at any time of the day or year [7]. Ecological monitoring as a fundamental component of wildlife conservation and management has become increasingly important, in the context of the growing anthropogenic pressure on wildlife, their habitats, and ecosystems in general [10,11]

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