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Detection of marine floating plastic using Sentinel-2 imagery and machine learning models
[article]
2021
arXiv
pre-print
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These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of marine floating plastic. ...
The present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying floating plastic debris in Mytilene (Greece), Limassol (Cyprus) ...
Acknowledgements This publication has emanated from research conducted with the financial support of Science ...
arXiv:2106.03694v2
fatcat:djvkxgnzc5cjlkpx3kb7riigrm
TOWARDS DETECTING FLOATING OBJECTS ON A GLOBAL SCALE WITH LEARNED SPATIAL FEATURES USING SENTINEL 2
2021
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this paper, we focus on detecting big patches of floating objects that can contain plastic as well as other materials with optical Sentinel 2 data. ...
Along with this work, we provide a hand-labeled Sentinel 2 dataset of floating objects on the sea surface and other water bodies such as lakes together with pre-trained deep learning models. ...
Following Biermann et al. (2020) , we optimized and predicted the shallow learning models on the designed FDI and NDVI feature space while the U-Net models used the raw input space of 12 Sentinel 2 bands ...
doi:10.5194/isprs-annals-v-3-2021-285-2021
fatcat:437ed3xxhrgyzbgspk2ztwqlg4
Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies
[chapter]
2018
Monitoring of Marine Pollution [Working Title]
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Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. ...
This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste ...
The authors would also like to acknowledge US Geological Survey for providing Landsat (TM, ETM+, and OLI) image archive, the Copernicus Open Access Hub for providing Sentinel-2 data, and the Hong Kong ...
doi:10.5772/intechopen.81657
fatcat:w3ipdmtikjhs7ez2bqo6uwlwaq
Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery
2021
Remote Sensing
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of floating plastics in selected Sentinel-2 grids for modelling. ...
This research focused on using high-resolution Sentinel-2 satellite remote sensing images to detect floating plastic debris in coastal waterbodies. ...
Acknowledgments: The authors are thankful to Kyriacos Themistocleous, Konstantinos Topouzelis, and Lauren Biermann for providing the validation set data used in the study. ...
doi:10.3390/rs13081598
fatcat:hmiptkbsa5hizlxmnmtwf7d6uy
MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data
2022
PLoS ONE
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Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. ...
MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships ...
Acknowledgments We would like to thank Professor Chuanmin Hu and Dr. Lauren Biermann for fruitful discussions about the spectral behaviour of Marine Debris. ...
doi:10.1371/journal.pone.0262247
pmid:34995337
pmcid:PMC8740969
fatcat:wftrakv6pjac7mkpcpnnopvcbu
Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea)
2020
Remote Sensing
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We verified findings with in situ data, recorded the spectral characteristics of floating plastic litter, and identified plastic debris trajectories and sources. ...
marine plastic debris. ...
Acknowledgments: We thank Kostas Tsiaras and Harilaos Kontoyiannis for useful discussions. We would like to thank Julio R. ...
doi:10.3390/rs12111727
fatcat:23jgubqzcngbdmsgvmj5jjpigy
Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes
2021
IEEE Access
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In [11] the spectral properties of three artificial floating plastic targets, as well as the surrounding seawater using Sentinel-1 and Sentinel-2 imagery, were investigated. ...
The authors detected and verified multiple floating plastic debris incidents using Planet, Sentinel-2, and Landsat-8 data by systematically assessing the spectral signatures from pure floating plastics ...
He is involved in international projects with European and national agencies, related to the monitoring of the water quality of inland and sea areas, land use and land cover applications. ...
doi:10.1109/access.2021.3073903
fatcat:bggvigmayfaqzahi6mnetjh5pe
Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery
2021
Remote Sensing
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Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. ...
We then compared Sentinel-2 and Worldview-3 satellite bands with these outcomes and identified 12 satellite bands to overlap with important wavelengths for discrimination between the classes. ...
Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2021, 13, 2335 ...
doi:10.3390/rs13122335
fatcat:i6j2uo5dirfmtczu223ge7442e
Optical Methods for Marine Litter Detection (OPTIMAL) - Final Report
2020
Zenodo
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The OPTIMAL project investigated the feasibility of Remote Sensing of Marine Litter, in particular marine plastic. ...
experimental and modelling phases, scientific impact and assessment, concluding with a forward look and final remarks. ...
The third experiment was an investigation using Sentinel-2 MSI imagery to test detection of accumulation of oating marine plastics. ...
doi:10.5281/zenodo.3748796
fatcat:iyqbr5kuavbfreqxswwe7u3wie
Satellite Monitoring of Terrestrial Plastic Waste
[article]
2022
arXiv
pre-print
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We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. ...
For each detected site, we algorithmically monitor waste site footprints through time and cross-reference other datasets to generate physical and social metadata. 19% of detected waste sites are located ...
[25] developed a classification method for both tire and plastic waste in Scotland using Sentinel-1 and Sentinel-2 data. ...
arXiv:2204.01485v1
fatcat:ho2t5mqkpvhwzoci2i62heoqlu
A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery
2020
Remote Sensing
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Here, we present an algorithm based on a series of filter thresholds to detect giant kelp employing Sentinel-2 imagery. ...
We employed the KD in our kelp filter algorithm to estimate the global extent of giant kelp and intertidal green algae per marine ecoregion and province, producing a high-resolution global map of giant ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs12040694
fatcat:qn2amo643jbrleopz24tj7xbci
Toward the Integrated Marine Debris Observing System
2019
Frontiers in Marine Science
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monitor the spread of plastic pollution and other marine debris. ...
Also, models are used to optimize the design of the system and, in turn, they will be gradually improved using the products of the system. ...
ACKNOWLEDGMENTS The authors would like to thank the editor, reviewers, and Frontiers Production staff, whose excellent help allowed to significantly improve the paper. ...
doi:10.3389/fmars.2019.00447
fatcat:7qynetrwxjhnzpi3qk3ni2ekim
Mediterranean Tapeweed Posidonia oceanica (L.) Delile, an Endangered Seagrass Species
2021
Egyptian Journal of Botany
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(Delile) because of its ability to create a three-dimensional habitat with high biodiversity and to build the "matte"(a terrace of interlaced rhizomes and roots trapping sediment). ...
This review aims to shed light on the importance of this plant, the extent of its dangerous status, and to urge the international community and governments to try to protect it in all possible ways, especially ...
A variety of machine learning algorithms such as Support Vector Machine (SVM), Random Forest, Maximum Likelihood, Classification, And Regression Trees (CART) were selected and evaluated to monitor the ...
doi:10.21608/ejbo.2021.67942.1652
fatcat:et5qzltwgfbehig26qajpqca3i
Satellite Remote Sensing in Shark and Ray Ecology, Conservation and Management
2019
Frontiers in Marine Science
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In addition, to facilitate the use of SRS in this field moving forward, we have compiled a list of popular SRS data sources and sensors for common environmental variables in marine science. ...
the remote nature of the marine ecosystems they inhabit. ...
The improved availability and resolution of free and open satellite data sources, such as ESA's Sentinel missions, and the increased use of derived and modeled SRS ocean products, will enhance the accuracy ...
doi:10.3389/fmars.2019.00135
fatcat:46h3izpwujex3lfbhabut5w7zq
Deep Learning and Earth Observation to Support the Sustainable Development Goals
[article]
2021
arXiv
pre-print
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The synergistic combination of deep learning models and Earth observation promises significant advances to support the sustainable development goals (SDGs). ...
This paper reviews current deep learning approaches for Earth observation data, along with their application towards monitoring and achieving the SDGs most impacted by the rapid development of deep learning ...
14.1.1 Coastal eutrophication and floating plastic debris density 14.2 Maps of marine and coastal ecosystems 14.3 14.3.1 Marine acidity (pH) 14.4 14.4.1 Geochemical (chlorophyll concentration) and geophysical ...
arXiv:2112.11367v1
fatcat:7eve5dr45vcublfqyzzrccuvxa
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