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
Open accessAbstract
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.
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The lack of teachers' AI literacy can be attributed to the over-intellectualization of primary education and the focus on achievement at the expense of the learning and teaching process, resulting in the neglect of teachers' AI literacy enhancement.
“the lack of teachers' AI literacy can be attributed to the over-intellectualization of primary education and the focus on achievement at the expense of the learning and teaching process, resulting in the neglect of teachers' AI literacy enhancement [76,77].”
From Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis (2022) · cites this paper as [77]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088 · cited 1× · see citing works
“To avoid the negative impact of the imbalanced class problem, we adopted the under-sampling method [66,67] to ensure that an equal number of samples was distributed between the 2 classes.”
MethodXanthos, D., & Walker, T. R. (2017) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2017.02.048 · Cited by 1,234 · see citing works
Marine litter within the European Marine Strategy Framework Directive
Galgani, F., Hanke, G., Werner, S., & Vrees, L. D. (2013) · ICES Journal of Marine Science · doi:10.1093/icesjms/fst122 · Cited by 716 · see citing works
Solutions for global marine litter pollution
Löhr, A., Savelli, H., Beunen, R., Kalz, M., Ragas, A. M. J., & Belleghem, F. V. (2017) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2017.08.009 · Cited by 386 · see citing works
Guidelines for the monitoring and assessment of plastic litter in the ocean
Kershaw, P. J., Turra, A., & Galgani, F. (2019) · Socio-Environmental Systems Modeling · Cited by 317 · see citing works
Toward the Integrated Marine Debris Observing System
Maximenko, N., Corradi, P., Law, K. L., van Sebille, E., Garaba, S. P., Lampitt, R. S., Galgani, F., Martínez-Vicente, V., Goddijn‐Murphy, L., Veiga, J. M., et al. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00447 · Cited by 312 · see citing works
Munari, C., Corbau, C., Simeoni, U., & Mistri, M. (2015) · Waste Management · doi:10.1016/j.wasman.2015.12.010 · Cited by 261 · see citing works
Simpson, R. J., Page, K., & Roure, D. D. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2579215 · Cited by 244 · see citing works
Vikas, M., & Dwarakish, G. S. (2015) · Aquatic Procedia · doi:10.1016/j.aqpro.2015.02.051 · Cited by 228 · see citing works
Topouzelis, K., Παπακωνσταντίνου, Α., & Garaba, S. P. (2019) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2019.03.011 · Cited by 223 · see citing works
Self-citationUse of unmanned aerial vehicles for efficient beach litter monitoring
Martin, C., Parkes, S. D., Zhang, Q., Zhang, X., McCabe, M. F., & Duarte, C. M. (2018) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2018.04.045 · Cited by 204 · see citing works
Fallati, L., Polidori, A., Salvatore, C., Saponari, L., Savini, A., & Galli, P. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.133581 · Cited by 189 · see citing works
Riihimäki, H., Luoto, M., & Heiskanen, J. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.01.030 · Cited by 178 · see citing works
Defining and Measuring Success in Online Citizen Science: A Case Study of Zooniverse Projects
Cox, J., Oh, E. Y., Simmons, B. D., Lintott, C. J., Masters, K. L., Greenhill, A., Graham, G., & Holmes, K. (2015) · Computing in Science & Engineering · doi:10.1109/mcse.2015.65 · Cited by 162 · see citing works
Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode
Taddia, Y., Stecchi, F., & Pellegrinelli, A. (2020) · Drones · doi:10.3390/drones4020009 · Cited by 161 · see citing works
Marine litter monitoring programmes—A review of methods with special reference to national surveys
Rees, G., & Pond, K. (1995) · Marine Pollution Bulletin · doi:10.1016/0025-326x(94)00192-c · Cited by 152 · see citing works
Riverine Plastic Litter Monitoring Using Unmanned Aerial Vehicles (UAVs)
Geraeds, M., van Emmerik, T. H. M., de Vries, R., & Razak, M. S. B. A. (2019) · Remote Sensing · doi:10.3390/rs11172045 · Cited by 152 · see citing works
Mapping marine litter using UAS on a beach-dune system: a multidisciplinary approach
Gonçalves, G. R., Andriolo, U., Pinto, L., et al. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.135742 · Cited by 143 · see citing works
Ferrari, R., McKinnon, D. H., He, H., Smith, R. N., Corke, P., González‐Rivero, M., Mumby, P. J., & Upcroft, B. (2016) · Remote Sensing · doi:10.3390/rs8020113 · Cited by 113 · see citing works
Topouzelis, K., Papageorgiou, D., Karagaitanakis, A., et al. (2020) · Remote Sensing · doi:10.3390/rs12122013 · Cited by 112 · see citing works
Self-citationIdentifying floating plastic marine debris using a deep learning approach
Kylili, K., Kyriakides, I., Artusi, A., & Hadjistassou, C. (2019) · Environmental Science and Pollution Research · doi:10.1007/s11356-019-05148-4 · Cited by 103 · see citing works
Marine litter, future prospects for research
Galgani, F. (2015) · Frontiers in Marine Science · doi:10.3389/fmars.2015.00087 · Cited by 102 · see citing works
Andriolo, U., Gonçalves, G. R., Bessa, F., et al. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.139632 · Cited by 98 · see citing works
Optimising beached litter monitoring protocols through aerial imagery
Deidun, A., Gauci, A., Lagorio, S., & Galgani, F. (2018) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2018.04.033 · Cited by 91 · see citing works
Bao, Z., Sha, J., Li, X., et al. (2018) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2018.08.009 · Cited by 90 · see citing works
Gonçalves, G. R., Andriolo, U., Pinto, L., et al. (2020) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2020.111158 · Cited by 89 · see citing works
Gonçalves, G. R., Andriolo, U., Gonçalves, L. M. S., et al. (2020) · Remote Sensing · doi:10.3390/rs12162599 · Cited by 87 · see citing works
Spatio-temporal variability of beached macro-litter on remote islands of the North Atlantic.
Ríos, N., Frias, J., Rodríguez, Y., Carriço, R., Garcia, S. M., Juliano, M., & Pham, C. (2018) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2018.05.038 · Cited by 74 · see citing works
Harris, P. T., & Baker, E. K. (2011) · Elsevier eBooks · doi:10.1016/b978-0-12-385140-6.00001-3 · Cited by 73 · see citing works
Monitoring methods for large micro- and meso-litter and applications at Baltic beaches
Haseler, M., Schernewski, G., Balčiūnas, A., et al. (2017) · Journal of Coastal Conservation · doi:10.1007/s11852-017-0497-5 · Cited by 73 · see citing works
OSPAR standard method and software for statistical analysis of beach litter data
Schulz, M., van Loon, W. M. G. M., Fleet, D. M., Baggelaar, P. K., & van der Meulen, E. (2017) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2017.06.045 · Cited by 70 · see citing works
Haarr, M. L., Westerveld, L., Fabrés, J., et al. (2018) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2018.12.025 · Cited by 66 · see citing works
Schulz, M., Clemens, T., Förster, H., Harder, T., Fleet, D. M., Gaus, S., Grave, C., Flegel, I., Schrey, E., & Hartwig, E. (2015) · Marine Environmental Research · doi:10.1016/j.marenvres.2015.04.007 · Cited by 61 · see citing works
Field test of beach litter assessment by commercial aerial drone
Lo, H., Wong, L. C., Kwok, S.-H., Lee, Y.-K., Po, B. H. K., Wong, C., Tam, N. F. Y., & Cheung, S. G. (2020) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2019.110823 · Cited by 55 · see citing works
Shades of grey: Marine litter research developments in Europe
Maes, T., PERRY, J. N., Alliji, K., Clarke, C., & Birchenough, S. N. R. (2019) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2019.06.019 · Cited by 54 · see citing works
A Protocol for Aerial Survey in Coastal Areas Using UAS
Doukari, M., Batsaris, M., Παπακωνσταντίνου, Α., et al. (2019) · Remote Sensing · doi:10.3390/rs11161913 · Cited by 46 · see citing works
Self-citationGalaxy Zoo: Morphological Classification and Citizen SciencePreprint
Fortson, L., Masters, K. L., Nichol, R. C., et al. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1104.5513 · Cited by 44 · see citing works
Husson, E., Reese, H., & Ecke, F. (2017) · Remote Sensing · doi:10.3390/rs9030247 · Cited by 44 · see citing works
An intelligent way for discerning plastics at the shorelines and the seas
Kylili, K., Hadjistassou, C., & Artusi, A. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-10105-7 · Cited by 40 · see citing works
Guideline for Monitoring Marine Litter on the Beaches in the OSPAR Maritime Area. Edition 1.0.
Wenneker, B., & Oosterbaan, L. (2010) · IOC of UNESCO (Intergovernmental Oceanographic Commission) · doi:10.25607/obp-968 · Cited by 40 · see citing works
Rip current evidence by hydrodynamic simulations, bathymetric surveys and UAV observation
Benassai, G., Aucelli, P. P. C., Budillon, G., et al. (2017) · Natural hazards and earth system sciences · doi:10.5194/nhess-17-1493-2017 · Cited by 37 · see citing works
Marine monitoring in Europe: is it adequate to address environmental threats and pressures?
Painting, S. J., Collingridge, K. A., Durand, D., et al. (2020) · Ocean science · doi:10.5194/os-16-235-2020 · Cited by 36 · see citing works
Conserve and sustainably use the oceans, seas and marine resources for sustainable development
Cicin‐Sain, B. (2015) · United Nations chronicle/UN chronicle · doi:10.18356/8fcfd5a1-en · Cited by 35 · see citing works
Nazerdeylami, A., Majidi, B., & Movaghar, A. (2020) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2020.105478 · Cited by 31 · see citing works
Παπακωνσταντίνου, Α., Stamati, C., & Topouzelis, K. (2020) · Remote Sensing · doi:10.3390/rs12030554 · Cited by 26 · see citing works
Self-citationMorseletto, P. (2020) · Marine Policy · doi:10.1016/j.marpol.2020.103956 · Cited by 26 · see citing works
Παπαχριστοπούλου, Ι., Filippides, A., Fakiris, E., & Papatheodorou, G. N. (2019) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2019.110684 · Cited by 25 · see citing works
Duarte, D., Andriolo, U., & Gonçalves, G. R. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-3-2020-439-2020 · Cited by 21 · see citing works
Mury, A., Collin, A., Houet, T., et al. (2020) · Drones · doi:10.3390/drones4020025 · Cited by 17 · see citing works
Costanzo, L. G., Marletta, G., & Alongi, G. (2020) · Journal of Marine Science and Engineering · doi:10.3390/jmse8090656 · Cited by 17 · see citing works
Chircop, A. E., Coffen-Smout, S., & McConnell, M. L. (2018) · Ocean Yearbook Online · doi:10.1163/22116001-03201028 · Cited by 16 · see citing works
An introduction to the ‘Oceans and Society: Blue Planet’ initiative
Smail, E. A., DiGiacomo, P. M., Seeyave, S., Djavidnia, S., Celliers, L., Traon, P. L., Gault, J., Escobar-Briones, E. G., Plag, H., Péquignet, C., et al. (2019) · Journal of Operational Oceanography · doi:10.1080/1755876x.2019.1634959 · Cited by 15 · see citing works
UAS close range remote sensing for mapping coastal environments
Παπακωνσταντίνου, Α., Topouzelis, K., & Doukari, M. (2017) · International Conference on Remote Sensing and Geoinformation of the Environment (RSCy) · doi:10.1117/12.2278988 · Cited by 13 · see citing works
Self-citationMapping refugee litters in the eastern coast of Lesvos using UAS, an emerging marine litter problem
Παπακωνσταντίνου, Α., Topouzelis, K., Doukari, M., et al. (2019) · Abstracts of the ICA · doi:10.5194/ica-abs-1-290-2019 · Cited by 6 · see citing works
Self-citationRip current evidence by hydrodynamic simulations, bathymetric surveys and UAV observationPreprint
Benassai, G., Aucelli, P. P. C., Budillon, G., et al. (2017) · doi:10.5194/nhess-2017-53 · Cited by 5 · see citing works
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- Authors
- Papakonstantinou, A., Batsaris, M., Spondylidis, S., & Topouzelis, K.
- Affiliations
- Department of Marine Sciences, University of the Aegean, 81100 Mytilene, Greece
- University of the Aegean (GR)
- Venue
- Drones
- Publisher
- MDPI AG
- Published
- 2021-01-18
- Volume
- 5
- Issue
- 1
- Pages
- 6
- ISSN
- 2504-446X (ISSN-L)
- OpenAlex
- W3123775691
- Cited by
- 99
- References
- 61
- Citations by year
- 2026: 16 · 2025: 15 · 2024: 15 · 2023: 14 · 2022: 23 · 2021: 16
- FWCI
- 4.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Drones (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Ktisis at Cyprus University of Technology (Cyprus University of Technology) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Environmental Science › Pollution › Microplastics and Plastic Pollution
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- marine litter, coastal areas, unmanned aerial systems, citizen science, litter detection, density mapping, convolutional neural networks, image classification, beach monitoring
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