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Remote Sensing
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Marine Mapping and Monitoring Using Autonomous Underwater Vehicles (AUVs)
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Marine Mapping and Monitoring Using Autonomous Underwater Vehicles (AUVs)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 20268

Special Issue Editors

Dr. Timm Schoening

E-Mail Website
Guest Editor
DeepSea Monitoring Group, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148 Kiel, Germany
Interests: image analysis; pattern recognition; machine learning; seafloor observation; high-performance computing; Autonomous Underwater Vehicles
Dr. Katleen Robert

E-Mail Website
Guest Editor
School of Ocean Technology, Fisheries and Marine Institute of Memorial University, St John's, NL A1C 5R3, Canada
Interests: seafloor and habitat mapping; cold-water corals; spatial ecology

Special Issue Information

Dear Colleagues,

Autonomous Underwater Vehicles (AUVs) can now provide an unprecedented increase in both resolution and seafloor coverage available for marine research. AUVs can cover large distances at high speeds or maneuver in complex terrain, and their low altitude above the seafloor enables data resolutions orders of magnitudes higher than ship-based sensors have been able to provide. Their ability to automatically map contiguous areas of interest, as well as to explore unknown territory using a range of survey approaches increases the cost-efficiency of ocean science. AUVs are also robust platforms for a variety of imaging sensors, like cameras and echosounders, as well as traditional ocean science gear like CTDs. More recent developments are incorporating novel technologies such as eDNA samplers or artificial intelligence for adaptive mission planning, increasing further the versatility of these platforms.

This Special Issue focuses on two aspects of AUVs. First are applications of these versatile platforms for mapping and monitoring the seafloor. This includes, amongst others, biological, geological, archaeological and infrastructure surveying tasks. The second focus is on the AUV platform itself: its efficient deployment to solve specific mapping or monitoring tasks, its evolving capabilities regarding sensor payload, its expected progress from the automated, (in essence pre-programmed), execution of a task towards true autonomy.

Dr. Timm Schoening
Dr. Katleen Robert
Guest Editors

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. Remote Sensing 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 2700 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

  • Autonomous Underwater Vehicles
  • seafloor mapping
  • marine monitoring
  • echosounders
  • cameras
  • true autonomy

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Published Papers (2 papers)

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Research

30 pages, 6089 KiB  
Article
Autonomous Underwater Monitoring System for Detecting Life on the Seabed by Means of Computer Vision Cloud Services
by Marouane Salhaoui, J. Carlos Molina-Molina, Antonio Guerrero-González, Mounir Arioua and Francisco J. Ortiz
Remote Sens. 2020, 12(12), 1981; https://doi.org/10.3390/rs12121981 - 19 Jun 2020
Cited by 26 | Viewed by 9923
Abstract
Autonomous underwater vehicles (AUVs) have increasingly played a key role in monitoring the marine environment, studying its physical-chemical parameters for the supervision of endangered species. AUVs now include a power source and an intelligent control system that allows them to autonomously carry out [...] Read more.
Autonomous underwater vehicles (AUVs) have increasingly played a key role in monitoring the marine environment, studying its physical-chemical parameters for the supervision of endangered species. AUVs now include a power source and an intelligent control system that allows them to autonomously carry out programmed tasks. Their navigation system is much more challenging than that of land-based applications, due to the lack of connected networks in the marine environment. On the other hand, due to the latest developments in neural networks, particularly deep learning (DL), the visual recognition systems can achieve impressive performance. Computer vision (CV) has especially improved the field of object detection. Although all the developed DL algorithms can be deployed in the cloud, the present cloud computing system is unable to manage and analyze the massive amount of computing power and data. Edge intelligence is expected to replace DL computation in the cloud, providing various distributed, low-latency and reliable intelligent services. This paper proposes an AUV model system designed to overcome latency challenges in the supervision and tracking process by using edge computing in an IoT gateway. The IoT gateway is used to connect the AUV control system to the internet. The proposed model successfully carried out a long-term monitoring mission in a predefined area of shallow water in the Mar Menor (Spain) to track the underwater Pinna nobilis (fan mussel) species. The obtained results clearly justify the proposed system’s design and highlight the cloud and edge architecture performances. They also indicate the need for a hybrid cloud/edge architecture to ensure a real-time control loop for better latency and accuracy to meet the system’s requirements. Full article
(This article belongs to the Special Issue Marine Mapping and Monitoring Using Autonomous Underwater Vehicles (AUVs))
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30 pages, 22724 KiB  
Article
The Autonomous Underwater Vehicle Integrated with the Unmanned Surface Vessel Mapping the Southern Ionian Sea. The Winning Technology Solution of the Shell Ocean Discovery XPRIZE
by Karolina Zwolak, Rochelle Wigley, Aileen Bohan, Yulia Zarayskaya, Evgenia Bazhenova, Wetherbee Dorshow, Masanao Sumiyoshi, Seeboruth Sattiabaruth, Jaya Roperez, Alison Proctor, Craig Wallace, Hadar Sade, Tomer Ketter, Benjamin Simpson, Neil Tinmouth, Robin Falconer, Ivan Ryzhov and Mohamed Elsaied Abou-Mahmoud
Remote Sens. 2020, 12(8), 1344; https://doi.org/10.3390/rs12081344 - 23 Apr 2020
Cited by 35 | Viewed by 8732
Abstract
The methods of data collection, processing, and assessment of the quality of the results of a survey conducted at the Southern Ionian Sea off the Messinian Peninsula, Greece are presented. Data were collected by the GEBCO-Nippon Foundation Alumni Team, competing in the Shell [...] Read more.
The methods of data collection, processing, and assessment of the quality of the results of a survey conducted at the Southern Ionian Sea off the Messinian Peninsula, Greece are presented. Data were collected by the GEBCO-Nippon Foundation Alumni Team, competing in the Shell Ocean Discovery XPRIZE, during the Final Round of the competition. Data acquisition was conducted by the means of unmanned vehicles only. The mapping system was composed of a single deep water AUV (Autonomous Underwater Vehicle), equipped with a high-resolution synthetic aperture sonar HISAS 1032 and multibeam echosounder EM 2040, partnered with a USV (Unmanned Surface Vessel). The USV provided positioning data as well as mapping the seafloor from the surface, using a hull-mounted multibeam echosounder EM 304. Bathymetry and imagery data were collected for 24 h and then processed for 48 h, with the extensive use of cloud technology and automatic data processing. Finally, all datasets were combined to generate a 5-m resolution bathymetric surface, as an example of the deep-water mapping capabilities of the unmanned vehicles’ cooperation and their sensors’ integration. Full article
(This article belongs to the Special Issue Marine Mapping and Monitoring Using Autonomous Underwater Vehicles (AUVs))
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