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Abstract
The ATLANTIS project aims to establish a pioneer pilot infrastructure that will allow the demonstration of key enabling robotic technologies for inspection and maintenance of offshore wind farms. The pilot will be implemented in Viana do Castelo, Portugal, and will allow for testing, validation and demonstration of technologies with a range of technology readiness level, in near-real/real environments.The demonstration of robotic technologies can promote the transition from traditional inspection and maintenance methodologies towards automated robotic strategies, that remove or reduce the need of human-in-the-loop, reducing costs and improving the safety of interventions. Eight scenarios, split into four showcases, will be used to determine the required developments for robotic integration and demonstrate the applicability in the inspection and maintenance processes. The scenarios considered were identified by end-users as key areas for robotics.

Abstract
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70-80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.

Abstract
This work addresses the use of hyperspectral imaging systems for remote detection of marine litter concentrations in oceanic environments. The work consisted on mounting an off-the-shelf hyperspectral imaging system (400-2500 nm) in two aerial platforms: manned and unmanned, and performing data acquisition to develop AI methods capable of detecting marine litter concentrations at the water surface. We performed the campaigns at Porto Pim Bay, Fail Island, Azores, resorting to artificial targets built using marine litter samples. During this work, we also developed a Convolutional Neural Network (CNN-3D), using spatial and spectral information to evaluate deep learning methods to detect marine litter in an automated manner. Results show over 84% overall accuracy (OA) in the detection and classification of the different types of marine litter samples present in the artificial targets.

Abstract
The oceans have the capability to support the current transitions occurring within our societies, including the implementation of clean energy production and storage technologies and new paths for sustainable food production. These transitions are, nonetheless, many times dependent on innovative technologies which require long paths of technology maturation before they can fit the existing ecosystems and markets. One critical step for technology validation is the demonstration stage in real offshore conditions, which is necessary to validate the performance of the proposed technologies, as well as their reliability and economic viability. In this respect, Portugal has been the testbed of several ocean-based technologies, including the Windfloat device, and possesses the necessary infrastructures to implement and test further innovative concepts and designs. Still, these infrastructures are currently underutilized, which means more technology developers could be testing and implementing their technologies in the country. This paper presents the OceanACT initiative, which is being led by five partners, + ATLANTIC, CEIIA, Fórum Oceano, INESC TEC and WavEC, aiming to promote and manage the existing offshore testing infrastructures in the country. The vision and the strategic path for the initiative, as well as the available infrastructures, and its respective metocean conditions, are presented here. This initiative intends to attract new technology developers to the country, and consequently generate relevant socioeconomic benefits, such as the attraction of investment, the inclusion of the national industry into the supply chain of these innovative projects, and the creation of highly qualified jobs.