The Role of Citizen Science and Deep Learning in Camera Trapping

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F21%3A00545740" target="_blank" >RIV/68145535:_____/21:00545740 - isvavai.cz</a>

Alternative codes found

RIV/70883521:28160/21:63532298 RIV/60460709:41330/21:85804

Result on the web

<a href="https://www.mdpi.com/2071-1050/13/18/10287" target="_blank" >https://www.mdpi.com/2071-1050/13/18/10287</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/su131810287" target="_blank" >10.3390/su131810287</a>

Result language

angličtina

Original language name

The Role of Citizen Science and Deep Learning in Camera Trapping

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10508 - Physical geography

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sustainability

ISSN

2071-1050

e-ISSN

2071-1050

Volume of the periodical

13

Issue of the periodical within the volume

18

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

10287

UT code for WoS article

000702047200001

EID of the result in the Scopus database

2-s2.0-85115118379