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Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images

2021-08Artigo científico Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências da Terra e do Ambiente
datacite.subject.fosCiências Agrárias::Agricultura, Silvicultura e Pescas
datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorAndriolo, Umberto
dc.contributor.authorGonçalves, Gil
dc.contributor.authorRangel-Buitrago, Nelson
dc.contributor.authorPaterni, Marco
dc.contributor.authorBessa, Filipa
dc.contributor.authorGonçalves, Luisa M. S.
dc.contributor.authorSobral, Paula
dc.contributor.authorBini, Monica
dc.contributor.authorDuarte, Diogo
dc.contributor.authorFontán-Bouzas, Ángela
dc.contributor.authorGonçalves, Diogo
dc.contributor.authorKataoka, Tomoya
dc.contributor.authorLuppichini, Marco
dc.contributor.authorPinto, Luis
dc.contributor.authorTopouzelis,Konstantinos
dc.contributor.authorVélez-Mendoza, Anubis
dc.contributor.authorMerlino, Silvia
dc.date.accessioned2026-02-20T12:11:16Z
dc.date.available2026-02-20T12:11:16Z
dc.date.issued2021-08
dc.description.abstractUnmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.5 and 0.7, depending on the litter parameter (type, material and colour) considered. Highest agreement was obtained for the type of items marked on the highest resolution image, among experts in litter surveys (W = 0.86), and within territorial subgroups (W = 0.85). Therefore, for a detailed categorization of litter on the environment, the MS should be performed by experienced and local operators, familiar with the most common type of litter present in the target area. This work provides insights for future operational improvements and optimizations of UAS-based images analysis to survey environmental pollution.eng
dc.description.sponsorshipThis work was supported by the Portuguese Foundation for Science and Technology (FCT) and by the European Regional Development Fund (FEDER) through COMPETE 2020, Operational Program for Competitiveness and Internationalization (POCI) in the framework of UIDB/00308/2020 and the research project UAS4Litter (PTDC/EAM-REM/30324/2017). U.A. and G.G. thank all the co-authors for the commitment in participating in this work, and for providing the high quality data necessary to perform the inter-operator concordance test. S.M. and M.P. thank the Park of Migliarino, Massacciuccoli and San Rossore for the permission to access the protected area and performing the field experience. The work of F.B. was supported by the University of Coimbra through contract IT057-18-7252. F.B. and P.S. acknowledge FCT, I.P. through the strategic project UIDB/04292/2020 granted to MARE. Luís Pinto was partially supported by the Centre for Mathematics of the University of Coimbra - UIDB/00324/2020, funded by the Portuguese Government through FCT/MCTES. A.F.-B. is supported by a Post-Doc Fellowship (ED481D2019/028) awarded by Xunta de Galicia (Spain). Thanks are also due to FCT/MCTES for the financial support to CESAM (UIDP/50017/2020 + UIDB/50017/2020), through national funds. Diogo Gonçalves was supported by the grants UI0308/UArribaS.1/2020 and UI0308-D.Remota1/2020 funded by the Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) with the support of Portuguese Foundation for Science and Technology (FCT) through national funds (PIDDAC) in the framework of UIDB/00308/2020. This paper was supported by a project (JPNP18016) commissioned by the New Energy and Industrial Technology Development Organization (NEDO) and the River Fund of the River Foundation (Grant Number: 2020-5211-041), Japan.
dc.identifier.citationUmberto Andriolo, Gil Gonçalves, Nelson Rangel-Buitrago, Marco Paterni, Filipa Bessa, Luisa M.S. Gonçalves, Paula Sobral, Monica Bini, Diogo Duarte, Ángela Fontán-Bouzas, Diogo Gonçalves, Tomoya Kataoka, Marco Luppichini, Luis Pinto, Konstantinos Topouzelis, Anubis Vélez-Mendoza, Silvia Merlino, Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images, Marine Pollution Bulletin, Volume 169, 2021, 112542, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2021.112542.
dc.identifier.doi10.1016/j.marpolbul.2021.112542
dc.identifier.eissn1879-3363
dc.identifier.issn0025-326X
dc.identifier.urihttp://hdl.handle.net/10400.8/15688
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationInstitute for Systems Engineering and Computers at Coimbra - INESC Coimbra
dc.relationLow-cost Unmanned Aerial Systems (UASs) for marine litter coastal mapping
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S0025326X21005762?via%3Dihub#ks0005
dc.relation.ispartofMarine Pollution Bulletin
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectPlastics
dc.subjectUnmanned aerial vehicle (UAV)
dc.subjectRemote sensing
dc.subjectWaste management
dc.subjectCoastal pollution
dc.titleDrones for litter mapping: An inter-operator concordance test in marking beached items on aerial imageseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstitute for Systems Engineering and Computers at Coimbra - INESC Coimbra
oaire.awardTitleLow-cost Unmanned Aerial Systems (UASs) for marine litter coastal mapping
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00308%2F2020/PT
oaire.awardURIhttp://hdl.handle.net/10400.8/13965
oaire.citation.endPage11
oaire.citation.startPage1
oaire.citation.titleMarine Pollution Bulletin
oaire.citation.volume169
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamConcurso para Financiamento de Projetos de Investigação Científica e Desenvolvimento Tecnológico em Todos os Domínios Científicos - 2017
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameGonçalves
person.givenNameLuisa
person.identifier.ciencia-id9116-82A0-3060
person.identifier.orcid0000-0002-6265-8903
person.identifier.ridU-1298-2017
person.identifier.scopus-author-id35145815700
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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relation.isAuthorOfPublication.latestForDiscovery1ba44699-bdda-4e01-97ec-c02fe603afc5
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Unmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.5 and 0.7, depending on the litter parameter (type, material and colour) considered. Highest agreement was obtained for the type of items marked on the highest resolution image, among experts in litter surveys (W = 0.86), and within territorial subgroups (W = 0.85). Therefore, for a detailed categorization of litter on the environment, the MS should be performed by experienced and local operators, familiar with the most common type of litter present in the target area. This work provides insights for future operational improvements and optimizations of UAS-based images analysis to survey environmental pollution.
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