Deep Learning Test Platform for Maritime Applications: Development of the eM/S Salama Unmanned Surface Vessel and Its Remote Operations Center for Sensor Data Collection and Algorithm Development - UTU Research Portal

A1 Refereed original research article in a scientific journal

Deep Learning Test Platform for Maritime Applications: Development of the eM/S Salama Unmanned Surface Vessel and Its Remote Operations Center for Sensor Data Collection and Algorithm Development

Authors: Kalliovaara, Juha; Jokela, Tero; Asadi, Mehdi; Majd, Amin; Hallio, Juhani; Auranen, Jani; Seppanen, Mika; Putkonen, Ari; Koskinen, Juho; Tuomola, Tommi; Mohammadi Moghaddam, Reza; Paavola, Jarkko

Publisher: MDPI

Publishing place: BASEL

Publication year: 2024

Journal: Remote Sensing

Journal name in source: REMOTE SENSING

Journal acronym: REMOTE SENS-BASEL

Article number: 1545

Volume: 16

Issue: 9

Number of pages: 35

eISSN: 2072-4292

DOI: https://doi.org/10.3390/rs16091545

Web address : https://doi.org/10.3390/rs16091545

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/457232565

Abstract

In response to the global megatrends of digitalization and transportation automation, Turku University of Applied Sciences has developed a test platform to advance autonomous maritime operations. This platform includes the unmanned surface vessel eM/S Salama and a remote operations center, both of which are detailed in this article. The article highlights the importance of collecting and annotating multi-modal sensor data from the vessel. These data are vital for developing deep learning algorithms that enhance situational awareness and guide autonomous navigation. By securing relevant data from maritime environments, we aim to enhance the autonomous features of unmanned surface vessels using deep learning techniques. The annotated sensor data will be made available for further research through open access. An image dataset, which includes synthetically generated weather conditions, is published alongside this article. While existing maritime datasets predominantly rely on RGB cameras, our work underscores the need for multi-modal data to advance autonomous capabilities in maritime applications.

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remotesensing-16-01545-v3.pdf

Funding information in the publication:
This research was co-funded by European Regional Development Fund, with the REACT EU support for regions to recover from the coronavirus pandemic, with grant numbers A78624 and A80845. This research was co-funded by the Finnish Ministry of Education and Culture in Applied Research Platform for Autonomous Systems (ARPA) project, 2020-2023. This research was co-funded by Business Finland in 5G-Advanced for Digitalization of Maritime Operations (ADMO) project.