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dc.contributor.authorMartin, Cecilia
dc.contributor.authorZhang, Qiannan
dc.contributor.authorZhai, Dongjun
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorDuarte, Carlos M.
dc.date.accessioned2021-03-04T06:44:16Z
dc.date.available2021-03-04T06:44:16Z
dc.date.issued2021-03-02
dc.date.submitted2020-07-20
dc.identifier.citationMartin, C., Zhang, Q., Zhai, D., Zhang, X., & Duarte, C. M. (2021). Enabling a large-scale assessment of litter along Saudi Arabian red sea shores by combining drones and machine learning. Environmental Pollution, 277, 116730. doi:10.1016/j.envpol.2021.116730
dc.identifier.issn0269-7491
dc.identifier.pmid33652184
dc.identifier.doi10.1016/j.envpol.2021.116730
dc.identifier.urihttp://hdl.handle.net/10754/667865
dc.description.abstractBeach litter assessments rely on time inefficient and high human cost protocols, mining the attainment of global beach litter estimates. Here we show the application of an emerging technique, the use of drones for acquisition of high-resolution beach images coupled with machine learning for their automatic processing, aimed at achieving the first national-scale beach litter survey completed by only one operator. The aerial survey had a time efficiency of 570 ± 40 m2 min-1 and the machine learning reached a mean (±SE) detection sensitivity of 59 ± 3% with high resolution images. The resulting mean (±SE) litter density on Saudi Arabian shores of the Red Sea is of 0.12 ± 0.02 litter items m-2, distributed independently of the population density in the area around the sampling station. Instead, accumulation of litter depended on the exposure of the beach to the prevailing wind and litter composition differed between islands and the main shore, where recreational activities are the major source of anthropogenic debris.
dc.description.sponsorshipWe thank Julia Reisser, Elena Corona, Núria Marbá and Dorte Krause-Jensen for help during fieldwork; the staff from the Coastal and Marine Resources core lab at KAUST for support during sampling cruises. We thank Matthew McCabe and Stephen Parkes for providing the DJI Phantom 3 Adv and the relative training. We thank anonymous reviewers and editors for the useful comments during the revision process.
dc.description.sponsorshipThis work was supported and funded by King Abdullah University of Science and Technology (KAUST) through the baseline funding of C.M.D. and X. Z.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0269749121003109
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Environmental pollution (Barking, Essex : 1987). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental pollution (Barking, Essex : 1987), [277, , (2021-03-02)] DOI: 10.1016/j.envpol.2021.116730 . © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleEnabling a large-scale assessment of litter along Saudi Arabian red sea shores by combining drones and machine learning.
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.contributor.departmentMarine Science Program
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.identifier.journalEnvironmental pollution (Barking, Essex : 1987)
dc.rights.embargodate2022-03-02
dc.eprint.versionPost-print
dc.identifier.volume277
dc.identifier.pages116730
kaust.personMartin, Cecilia
kaust.personZhang, Qiannan
kaust.personZhai, Dongjun
kaust.personZhang, Xiangliang
kaust.personDuarte, Carlos M.
dc.date.accepted2021-02-09
refterms.dateFOA2021-03-04T08:27:08Z
kaust.acknowledged.supportUnitcore lab


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