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dc.contributor.authorMartin, Cecilia
dc.contributor.authorParkes, Stephen
dc.contributor.authorZhang, Qiannan
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorMcCabe, Matthew
dc.contributor.authorDuarte, Carlos M.
dc.date.accessioned2018-05-14T13:37:07Z
dc.date.available2018-05-14T13:37:07Z
dc.date.issued2018-05-05
dc.identifier.citationMartin C, Parkes S, Zhang Q, Zhang X, McCabe MF, et al. (2018) Use of unmanned aerial vehicles for efficient beach litter monitoring. Marine Pollution Bulletin 131: 662–673. Available: http://dx.doi.org/10.1016/j.marpolbul.2018.04.045.
dc.identifier.issn0025-326X
dc.identifier.doi10.1016/j.marpolbul.2018.04.045
dc.identifier.urihttp://hdl.handle.net/10754/627872
dc.description.abstractA global beach litter assessment is challenged by use of low-efficiency methodologies and incomparable protocols that impede data integration and acquisition at a national scale. The implementation of an objective, reproducible and efficient approach is therefore required. Here we show the application of a remote sensing based methodology using a test beach located on the Saudi Arabian Red Sea coastline. Litter was recorded via image acquisition from an Unmanned Aerial Vehicle, while an automatic processing of the high volume of imagery was developed through machine learning, employed for debris detection and classification in three categories. Application of the method resulted in an almost 40 times faster beach coverage when compared to a standard visual-census approach. While the machine learning tool faced some challenges in correctly detecting objects of interest, first classification results are promising and motivate efforts to further develop the technique and implement it at much larger scales.
dc.description.sponsorshipThis work was supported and funded by King Abdullah University of Science and Technology (KAUST) through the baseline funding of CMD and by KAUST Office of Sponsored Research (OSR) under Award No. 2639. We thank the crew of R/V Thuwal, the Coastal and Marine Resources Core Lab and Red Sea Research Center colleagues for field assistance. We particularly thank Mohammed Magbool Aljahdali, Núria Marbà and Dorte Krause-Jensen for support during field-work.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0025326X18302765
dc.subjectMarine debris
dc.subjectPlastic pollution
dc.subjectCoastline
dc.subjectUAV
dc.subjectMachine learning
dc.titleUse of unmanned aerial vehicles for efficient beach litter monitoring
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentMarine Science Program
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.identifier.journalMarine Pollution Bulletin
kaust.personMartin, Cecilia
kaust.personParkes, Stephen
kaust.personZhang, Qiannan
kaust.personZhang, Xiangliang
kaust.personMcCabe, Matthew
kaust.personDuarte, Carlos M.
kaust.grant.number2639
dc.date.published-online2018-05-05
dc.date.published-print2018-06


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