A Cloud-Based Framework for Large-Scale Monitoring of Ocean Plastics Using Multi-Spectral Satellite Imagery and Generative Adversarial Network

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dc.authoridMahdianpari, Masoud/0000-0002-7234-959X
dc.authoridJamali, Ali/0000-0002-6073-5493
dc.contributor.authorJamali, Ali
dc.contributor.authorMahdianpari, Masoud
dc.date.accessioned2024-09-29T16:08:16Z
dc.date.available2024-09-29T16:08:16Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMarine debris is considered a threat to the inhabitants, as well as the marine environments. Accumulation of marine debris, besides climate change factors, including warming water, sea-level rise, and changes in oceans' chemistry, are causing the potential collapse of the marine environment's health. Due to the increase of marine debris, including plastics in coastlines, ocean and sea surfaces, and even in deep ocean layers, there is a need for developing new advanced technology for the detection of large-sized marine pollution (with sizes larger than 1 m) using state-of-the-art remote sensing and machine learning tools. Therefore, we developed a cloud-based framework for large-scale marine pollution detection with the integration of Sentinel-2 satellite imagery and advanced machine learning tools on the Sentinel Hub cloud application programming interface (API). Moreover, we evaluated the performance of two shallow machine learning algorithms of random forest (RF) and support vector machine (SVM), as well as the deep learning method of the generative adversarial network-random forest (GAN-RF) for the detection of ocean plastics in the pilot site of Mytilene Island, Greece. Based on the obtained results, the shallow algorithms of RF and SVM achieved an overall accuracy of 88% and 84%, respectively, with available training data of plastic debris. The GAN-RF classifier improved the detection of ocean plastics of the RF method by 8%, achieving an overall accuracy of 96% by generating several synthetic ocean plastic samples.en_US
dc.identifier.doi10.3390/w13182553
dc.identifier.issn2073-4441
dc.identifier.issue18en_US
dc.identifier.scopus2-s2.0-85115435905en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/w13182553
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7441
dc.identifier.volume13en_US
dc.identifier.wosWOS:000700526600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofWateren_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectocean plasticsen_US
dc.subjectsupport vector machineen_US
dc.subjectrandom foresten_US
dc.subjectmarine debrisen_US
dc.subjectmarine pollutionen_US
dc.subjectSentinel Huben_US
dc.subjectgenerative adversarial networken_US
dc.titleA Cloud-Based Framework for Large-Scale Monitoring of Ocean Plastics Using Multi-Spectral Satellite Imagery and Generative Adversarial Networken_US
dc.typeArticleen_US

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