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Current Progress on Marine Microplastics Pollution Research: A Review on Pollution Occurrence, Detection, and Environmental Effects
 
 
Article

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

1
Civil Engineering Department, Faculty of Engineering, University of Karabük, Karabük 78050, Turkey
2
Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Ines Martins, Irene Martins and Joana Raimundo
Water 2021, 13(18), 2553; https://doi.org/10.3390/w13182553
Received: 24 August 2021 / Revised: 15 September 2021 / Accepted: 16 September 2021 / Published: 17 September 2021
(This article belongs to the Special Issue Microplastics Pollution in Marine Environment)
Marine 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. View Full-Text
Keywords: ocean plastics; support vector machine; random forest; marine debris; marine pollution; Sentinel Hub; generative adversarial network ocean plastics; support vector machine; random forest; marine debris; marine pollution; Sentinel Hub; generative adversarial network
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MDPI and ACS Style

Jamali, A.; Mahdianpari, M. A Cloud-Based Framework for Large-Scale Monitoring of Ocean Plastics Using Multi-Spectral Satellite Imagery and Generative Adversarial Network. Water 2021, 13, 2553. https://doi.org/10.3390/w13182553

AMA Style

Jamali A, Mahdianpari M. A Cloud-Based Framework for Large-Scale Monitoring of Ocean Plastics Using Multi-Spectral Satellite Imagery and Generative Adversarial Network. Water. 2021; 13(18):2553. https://doi.org/10.3390/w13182553

Chicago/Turabian Style

Jamali, Ali, and Masoud Mahdianpari. 2021. "A Cloud-Based Framework for Large-Scale Monitoring of Ocean Plastics Using Multi-Spectral Satellite Imagery and Generative Adversarial Network" Water 13, no. 18: 2553. https://doi.org/10.3390/w13182553

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