ERISNet: Deep learning network for Sargassum detection along the coastline of the Mexican Caribbean
A peer-reviewed article of this Preprint also exists.
Author and article information
Abstract
Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep learning network (named ERISNet) was designed specifically to detect this macroalgae along the coastline through remote sensing support. A new dataset which includes pixels values with and without Sargassum was built to training and testing ERISNet. Aqua-MODIS imagery was used to build the dataset. After the learning process, the designed algorithm achieves a 90 % of probability in its classification skills. ERISNet provides a baseline for automated systems to accurately and efficiently monitor algal blooms arrivals.
Cite this as
2018. ERISNet: Deep learning network for Sargassum detection along the coastline of the Mexican Caribbean. PeerJ Preprints 6:e27445v1 https://doi.org/10.7287/peerj.preprints.27445v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
Author comment
This is a submission to PeerJ for review.
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Supplemental Information
Training dataset for proposed algorithm
The dataset included 14 different attributes and 4515 instances, of which 2306 corresponded to the presence of Sargassum and 2209 without. Dataset was built with the pixel data of each MODIS-band for different dates.
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Javier Arellano-Verdejo conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft, software.
Hugo-Enrique Lazcano-Hernandez conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft, dataset.
Nancy Cabanillas-Terán analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
The raw measurements are provided in the supplementary file 1
Funding
The authors received no funding for this work.
