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Water
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Pattern Analysis, Recognition and Classification of Marine Data
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Pattern Analysis, Recognition and Classification of Marine Data

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Oceans and Coastal Zones".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 11831

Special Issue Editor

Dr. Simone Marini

E-Mail Website
Guest Editor
1. Institute of Marine Sciences (ISMAR) of the National Research Council (CNR), Lerici, Italy
2. Stazione Zoologica Anton Dohrn (SZN), Naples, Italy
Interests: data science; artificial intelligence; machine learning; knowledge discovery; ocean observation; marine science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing interest of the marine science community in data science and artificial intelligence deals mainly with the expansion of the scope and scale of ocean observations. Similarly, the ubiquitous increment of automated sampling approaches based on smart sensors leads to the continuous flood of heterogeneous data. The huge amount of data produced by the ocean observing systems is fostered by the complexity of the aquatic environment, from which heterogeneous and environmental measurements are needed to explain the physical, biological, and chemical characteristics of the ocean and the functioning of the marine ecosystems.

Approaches for pattern analysis, recognition, and classification of marine data are urgently needed to extract knowledge from the acquired measurements, to define and develop intelligent services capable of managing the forthcoming new generation of intelligent observatories and vehicles and define and develop novel integrated models for understanding and predicting the ocean health status.

This Special Issue welcomes original scientific contributions and survey papers in the field of pattern analysis, recognition, and classification of marine data that include, but are not limited to the following: image, video, and acoustic content-based recognition and classification; 3D modeling and analysis; remote sensing and eDNA pattern analysis and classification; knowledge discovery; multivariate analysis; data fusion; decision-making algorithms; predictive modeling; automated logical reasoning.

Dr. Simone Marini
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data science
  • artificial intelligence
  • machine learning
  • ocean monitoring
  • intelligent underwater observatories
  • underwater internet of things
  • remote sensing
  • predictive modeling

Published Papers (3 papers)

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Research

15 pages, 3448 KiB  
Article
Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images
by Silvia Merlino, Marco Paterni, Marina Locritani, Umberto Andriolo, Gil Gonçalves and Luciano Massetti
Water 2021, 13(23), 3349; https://0-doi-org.brum.beds.ac.uk/10.3390/w13233349 - 25 Nov 2021
Cited by 29 | Viewed by 4729
Abstract
Unmanned aerial vehicles (UAV, aka drones) are being used for mapping macro-litter in the environment. As drone images require a manual processing task for detecting marine litter, it is of interest to evaluate the accuracy of non-expert citizen science operators (CSO) in performing [...] Read more.
Unmanned aerial vehicles (UAV, aka drones) are being used for mapping macro-litter in the environment. As drone images require a manual processing task for detecting marine litter, it is of interest to evaluate the accuracy of non-expert citizen science operators (CSO) in performing this task. Students from Italian secondary schools (in this work, the CSO) were invited to identify, mark, and classify stranded litter items on a UAV orthophoto collected on an Italian beach. A specific training program and working tools were developed for the aim. The comparison with the standard in situ visual census survey returned a general underestimation (50%) of items. However, marine litter bulk categorisation was fairly in agreement with the in situ survey, especially for sources classification. The concordance level among CSO ranged between 60% and 91%, depending on the item properties considered (type, material, and colour). As the assessment accuracy was in line with previous works developed by experts, remote detection of marine litter on UAV images can be improved through citizen science programs, upon an appropriate training plan and provision of specific tools. Full article
(This article belongs to the Special Issue Pattern Analysis, Recognition and Classification of Marine Data)
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17 pages, 3533 KiB  
Article
Machine Learning Applications of Convolutional Neural Networks and Unet Architecture to Predict and Classify Demosponge Behavior
by Dominica Harrison, Fabio Cabrera De Leo, Warren J. Gallin, Farin Mir, Simone Marini and Sally P. Leys
Water 2021, 13(18), 2512; https://0-doi-org.brum.beds.ac.uk/10.3390/w13182512 - 13 Sep 2021
Cited by 11 | Viewed by 3324
Abstract
Biological data sets are increasingly becoming information-dense, making it effective to use a computer science-based analysis. We used convolution neural networks (CNN) and the specific CNN architecture Unet to study sponge behavior over time. We analyzed a large time series of hourly high-resolution [...] Read more.
Biological data sets are increasingly becoming information-dense, making it effective to use a computer science-based analysis. We used convolution neural networks (CNN) and the specific CNN architecture Unet to study sponge behavior over time. We analyzed a large time series of hourly high-resolution still images of a marine sponge, Suberites concinnus (Demospongiae, Suberitidae) captured between 2012 and 2015 using the NEPTUNE seafloor cabled observatory, off the west coast of Vancouver Island, Canada. We applied semantic segmentation with the Unet architecture with some modifications, including adapting parts of the architecture to be more applicable to three-channel images (RGB). Some alterations that made this model successful were the use of a dice-loss coefficient, Adam optimizer and a dropout function after each convolutional layer which provided losses, accuracies and dice scores of up to 0.03, 0.98 and 0.97, respectively. The model was tested with five-fold cross-validation. This study is a first step towards analyzing trends in the behavior of a demosponge in an environment that experiences severe seasonal and inter-annual changes in climate. The end objective is to correlate changes in sponge size (activity) over seasons and years with environmental variables collected from the same observatory platform. Our work provides a roadmap for others who seek to cross the interdisciplinary boundaries between biology and computer science. Full article
(This article belongs to the Special Issue Pattern Analysis, Recognition and Classification of Marine Data)
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22 pages, 4826 KiB  
Article
Sea Topography of the Ionian and Adriatic Seas Using Repeated GNSS Measurements
by Sotiris Lycourghiotis
Water 2021, 13(6), 812; https://0-doi-org.brum.beds.ac.uk/10.3390/w13060812 - 16 Mar 2021
Cited by 2 | Viewed by 2767
Abstract
The mean sea surface topography of the Ionian and Adriatic Seas has been determined. This was based on six-months of Global Navigation Satellite System (GNSS) measurements which were performed on the Ionian Queen (a ship). The measurements were analyzed following a double-path methodology [...] Read more.
The mean sea surface topography of the Ionian and Adriatic Seas has been determined. This was based on six-months of Global Navigation Satellite System (GNSS) measurements which were performed on the Ionian Queen (a ship). The measurements were analyzed following a double-path methodology based on differential GNSS (D-GNSS) and precise point positioning (PPP) analysis. Numerical filtering techniques, multi-parametric accuracy analysis and a new technique for removing the meteorological tide factors were also used. Results were compared with the EGM96 geoid model. The calculated differences ranged between 0 and 48 cm. The error of the results was estimated to fall within 3.31 cm. The 3D image of the marine topography in the region shows a nearly constant slope of 4 cm/km in the N–S direction. Thus, the effectiveness of the approach “repeated GNSS measurements on the same route of a ship” developed in the context of “GNSS methods on floating means” has been demonstrated. The application of this approach using systematic multi-track recordings on conventional liner ships is very promising, as it may open possibilities for widespread use of the methodology across the world. Full article
(This article belongs to the Special Issue Pattern Analysis, Recognition and Classification of Marine Data)
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