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Collective view: mapping Sargassum distribution along beaches
The atypical arrival of pelagic Sargassum to the Mexican Caribbean beaches has caused considerable economic and ecological damage. Furthermore, it has raised new challenges for monitoring the coastlines. Historically, satellite remote-sensing has been used for Sargassum monitoring in the ocean; none...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157248/ https://www.ncbi.nlm.nih.gov/pubmed/34084930 http://dx.doi.org/10.7717/peerj-cs.528 |
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author | Arellano-Verdejo, Javier Lazcano-Hernández, Hugo E. |
author_facet | Arellano-Verdejo, Javier Lazcano-Hernández, Hugo E. |
author_sort | Arellano-Verdejo, Javier |
collection | PubMed |
description | The atypical arrival of pelagic Sargassum to the Mexican Caribbean beaches has caused considerable economic and ecological damage. Furthermore, it has raised new challenges for monitoring the coastlines. Historically, satellite remote-sensing has been used for Sargassum monitoring in the ocean; nonetheless, limitations in the temporal and spatial resolution of available satellite platforms do not allow for near real-time monitoring of this macro-algae on beaches. This study proposes an innovative approach for monitoring Sargassum on beaches using Crowdsourcing for imagery collection, deep learning for automatic classification, and geographic information systems for visualizing the results. We have coined this collaborative process “Collective View”. It offers a geotagged dataset of images illustrating the presence or absence of Sargassum on beaches located along the northern and eastern regions in the Yucatan Peninsula, in Mexico. This new dataset is the largest of its kind in surrounding areas. As part of the design process for Collective View, three convolutional neural networks (LeNet-5, AlexNet and VGG16) were modified and retrained to classify images, according to the presence or absence of Sargassum. Findings from this study revealed that AlexNet demonstrated the best performance, achieving a maximum recall of 94%. These results are good considering that the training was carried out using a relatively small set of unbalanced images. Finally, this study provides a first approach to mapping the Sargassum distribution along the beaches using the classified geotagged images and offers novel insight into how we can accurately map the arrival of algal blooms along the coastline. |
format | Online Article Text |
id | pubmed-8157248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81572482021-06-02 Collective view: mapping Sargassum distribution along beaches Arellano-Verdejo, Javier Lazcano-Hernández, Hugo E. PeerJ Comput Sci Artificial Intelligence The atypical arrival of pelagic Sargassum to the Mexican Caribbean beaches has caused considerable economic and ecological damage. Furthermore, it has raised new challenges for monitoring the coastlines. Historically, satellite remote-sensing has been used for Sargassum monitoring in the ocean; nonetheless, limitations in the temporal and spatial resolution of available satellite platforms do not allow for near real-time monitoring of this macro-algae on beaches. This study proposes an innovative approach for monitoring Sargassum on beaches using Crowdsourcing for imagery collection, deep learning for automatic classification, and geographic information systems for visualizing the results. We have coined this collaborative process “Collective View”. It offers a geotagged dataset of images illustrating the presence or absence of Sargassum on beaches located along the northern and eastern regions in the Yucatan Peninsula, in Mexico. This new dataset is the largest of its kind in surrounding areas. As part of the design process for Collective View, three convolutional neural networks (LeNet-5, AlexNet and VGG16) were modified and retrained to classify images, according to the presence or absence of Sargassum. Findings from this study revealed that AlexNet demonstrated the best performance, achieving a maximum recall of 94%. These results are good considering that the training was carried out using a relatively small set of unbalanced images. Finally, this study provides a first approach to mapping the Sargassum distribution along the beaches using the classified geotagged images and offers novel insight into how we can accurately map the arrival of algal blooms along the coastline. PeerJ Inc. 2021-05-13 /pmc/articles/PMC8157248/ /pubmed/34084930 http://dx.doi.org/10.7717/peerj-cs.528 Text en © 2021 Arellano-Verdejo and Lazcano-Hernández https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Arellano-Verdejo, Javier Lazcano-Hernández, Hugo E. Collective view: mapping Sargassum distribution along beaches |
title | Collective view: mapping Sargassum distribution along beaches |
title_full | Collective view: mapping Sargassum distribution along beaches |
title_fullStr | Collective view: mapping Sargassum distribution along beaches |
title_full_unstemmed | Collective view: mapping Sargassum distribution along beaches |
title_short | Collective view: mapping Sargassum distribution along beaches |
title_sort | collective view: mapping sargassum distribution along beaches |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157248/ https://www.ncbi.nlm.nih.gov/pubmed/34084930 http://dx.doi.org/10.7717/peerj-cs.528 |
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