Use of unmanned aerial vehicles for efficient beach litter monitoring

Handle URI:
http://hdl.handle.net/10754/627872
Title:
Use of unmanned aerial vehicles for efficient beach litter monitoring
Authors:
Martin, Cecilia ( 0000-0002-3886-2824 ) ; Parkes, Stephen; Zhang, Qiannan; Zhang, Xiangliang ( 0000-0002-3574-5665 ) ; McCabe, Matthew ( 0000-0002-1279-5272 ) ; Duarte, Carlos M. ( 0000-0002-1213-1361 )
Abstract:
A 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.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Marine Science Program; Water Desalination and Reuse Research Center (WDRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Environmental Science and Engineering Program; Red Sea Research Center (RSRC)
Citation:
Martin 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.
Publisher:
Elsevier BV
Journal:
Marine Pollution Bulletin
KAUST Grant Number:
2639
Issue Date:
5-May-2018
DOI:
10.1016/j.marpolbul.2018.04.045
Type:
Article
ISSN:
0025-326X
Sponsors:
This 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.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0025326X18302765
Appears in Collections:
Articles; Red Sea Research Center (RSRC); Environmental Science and Engineering Program; Marine Science Program; Computer Science Program; Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMartin, Ceciliaen
dc.contributor.authorParkes, Stephenen
dc.contributor.authorZhang, Qiannanen
dc.contributor.authorZhang, Xiangliangen
dc.contributor.authorMcCabe, Matthewen
dc.contributor.authorDuarte, Carlos M.en
dc.date.accessioned2018-05-14T13:37:07Z-
dc.date.available2018-05-14T13:37:07Z-
dc.date.issued2018-05-05en
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.en
dc.identifier.issn0025-326Xen
dc.identifier.doi10.1016/j.marpolbul.2018.04.045en
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.en
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.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0025326X18302765en
dc.subjectMarine debrisen
dc.subjectPlastic pollutionen
dc.subjectCoastlineen
dc.subjectUAVen
dc.subjectMachine learningen
dc.titleUse of unmanned aerial vehicles for efficient beach litter monitoringen
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentMarine Science Programen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentRed Sea Research Center (RSRC)en
dc.identifier.journalMarine Pollution Bulletinen
kaust.authorMartin, Ceciliaen
kaust.authorParkes, Stephenen
kaust.authorZhang, Qiannanen
kaust.authorZhang, Xiangliangen
kaust.authorMcCabe, Matthewen
kaust.authorDuarte, Carlos M.en
kaust.grant.number2639en
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