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A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

1
Department of Marine Sciences, University of the Aegean, 81100 Mytilene, Greece
2
Geography Department, University of the Aegean, 81100 Mytilene, Greece
*
Author to whom correspondence should be addressed.
Received: 16 December 2020 / Revised: 13 January 2021 / Accepted: 14 January 2021 / Published: 18 January 2021
Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps. View Full-Text
Keywords: unmanned aerial systems; marine litter; deep learning; convolutional neural networks; computer vision; marine litter detection unmanned aerial systems; marine litter; deep learning; convolutional neural networks; computer vision; marine litter detection
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MDPI and ACS Style

Papakonstantinou, A.; Batsaris, M.; Spondylidis, S.; Topouzelis, K. A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone. Drones 2021, 5, 6. https://doi.org/10.3390/drones5010006

AMA Style

Papakonstantinou A, Batsaris M, Spondylidis S, Topouzelis K. A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone. Drones. 2021; 5(1):6. https://doi.org/10.3390/drones5010006

Chicago/Turabian Style

Papakonstantinou, Apostolos, Marios Batsaris, Spyros Spondylidis, and Konstantinos Topouzelis. 2021. "A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone" Drones 5, no. 1: 6. https://doi.org/10.3390/drones5010006

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