Big Data Management in Maritime Transport

As maritime transport produces a large amount of data from various sources and in different formats, authors have analysed current applications of Big Data by researching global applications and experiences and by studying journal and conference articles. Big Data innovations in maritime transport (both cargo and passenger) are demonstrated, mainly in the fields of seaport operations, weather routing, monitoring/tracking and security. After the analysis, the authors have concluded that Big Data analyses can provide deep understanding of causalities and correlations in maritime transport, thus improving decision making. However, there exist major challenges of an efficient data collection and processing in maritime transport, such as technology challenges, challenges due to competitive conditions etc. Finally, the authors provide a future perspective of Big Data usage in maritime transport

No Ground Truth at Sea – Developing High-Accuracy AI Decision-Support for Complex Environments

As AI decision-support systems are increasingly developed for applications outside of traditional organizational confinements, developers are confronted with new sources of complexity they need to address. However, we know little about how AI applications are developed for natural use domains with high environmental complexity, stemming from physical influences outside of the developers’ control. This study investigates what challenges emerge from such complexity and how developers mitigate them. Drawing upon a rich longitudinal single-case study on the development of AI decision-support for maritime navigation, findings show that achieving high output accuracy is complicated by the physical environment hindering training data creation. Further, developers chose to reduce the output accuracy and adapt the HMI design to successfully situate the AI application in an existing sociotechnical context. This study contributes to IS literature following recent calls for phenomenon-based examination of emerging challenges when extending the scope frontier of AI and provides practical recommendations for developing AI decision-support for complex environments

An analysis of the factors inhibiting ECDIS from effectually achieving its intended primary function of contributing to safe navigation

This research is contextualised in the maritime domain, where since the introduction of legislation mandating the carriage of Electronic Chart Display and Information Systems (ECDIS) by merchant vessels, evidence has emerged of unintended consequences of this legislation – which threaten the safety of navigation. The real-time presentation of information displayed by ECDIS should improve deck officers’ cognitive assessment of their navigational situation, yet the terms ‘ECDIS-assisted accidents’ and ‘ECDIS-assisted groundings’ have of late become part of maritime terminology. This dissertation presents an analysis of the factors inhibiting ECDIS from effectually achieving its intended primary function of contributing to safe navigation. Applicable legislation is identified and case studies are used to scrutinise the efficacy of the current legal framework regulating the use of ECDIS. The potentially unsafe technical operational aspects and limitations of ECDIS are analysed and the human factor and human error in the use of ECDIS are critically evaluated. Current industry initiatives to improve the safety of navigation with ECDIS are outlined and additional measures to mitigate unsafe practices in the use of ECDIS by deck officers are considered. This research finds that despite an apparently robust legal framework regulating the use of ECDIS, the current legislative provisions do not appear to be effective in preventing ECDIS-assisted accidents, particularly vessel groundings. It is found that ECDIS training has not been sufficiently integrated into the STCW Code and express provisions mandating how ECDIS should be used as an aid to navigation are inadequate. Overreliance is identified as a primary risk in the use of ECDIS, as it significantly reduces navigational safety. ECDIS is an aid to navigation and must be used in conjunction with traditional watchkeeping skills and the practices of good seamanship. Given that most maritime casualties are caused by human error, measures to address the human factor should be embedded into ECDIS pedagogy. Instead of fulfilling its primary function of improving the safety of navigation, the use of ECDIS can in fact reduce situational awareness by distracting navigators from looking out of the bridge windows. This research concludes that in the case of ECDIS, the introduction of technology intended to reduce human error in shipboard operations has inadvertently created new error sources. Improved training methods are required to address these types of technologically-generated error pathways

Big Data Management in the Shipping Industry: Examining Strengths Vs Weaknesses and Highlighting Relevant Business Opportunities

History testifies that there is a dialectic relationship between humans and technology. Especially during the last couple of decades, the shipping industry has benefitted from a very extended number of advanced technology innovations. Today, all systems supporting the conduct of navigation and the various information technology (IT) applications related to ship management activities are heavily reliant upon (almost) real-time information to safely/effectively fulfil their allocated tasks. As a result, truly vast quantities of data -which are often described as “Big Data” in the wider literature- are created and the issue of how to effectively manage all the associated information is clearly standing out. Furthermore, topics such as optimising the conduct of all relevant activities on-board the vessel at sea, identifying the right opportunities in order to further promote business and boost profits, or even contributing to the numerous elements of sustainability by achieving reductions in energy consumption and/or a better environmental footprint for shipping, should all be researched further. Considering the quite limited capacity of the human brain to process really enormous quantities of data in comparison to modern computers, the trend to use advanced software tools for extracting and processing the “right” information that is often hidden in the vast pool of Big Data, as well as relying on advanced techniques and algorithms to perform the relevant statistical analysis becomes quite obvious. The purpose of this paper, which follows a qualitative approach working in unison with a “Strengths, Weaknesses, Opportunities, and Threats” (SWOT) analysis, is to identify and briefly discuss the most relevant tools and techniques that are associated with Big Data Management. It will also clearly highlight the various benefits that are opening up and will try to explain the notion behind this transition to a new era, characterized by the term “smart shipping”. A very important conclusion is that the exploitation of Big Data and the role of certain software applications in accessing and managing this large volume of information are key factors for improving/optimising the conduct of ship operations and management; establishment of a “Data Driven Culture” within a shipping company can clearly improve the current business model and at the same time promote sustainabilit

BoatNet: automated small boat composition detection using deep learning on satellite imagery

Tracking and measuring national carbon footprints is key to achieving the ambitious goals set by the Paris Agreement on carbon emissions. According to statistics, more than 10% of global transportation carbon emissions result from shipping. However, accurate tracking of the emissions of the small boat segment is not well established. Past research looked into the role played by small boat fleets in terms of greenhouse gases, but this has relied either on high-level technological and operational assumptions or the installation of global navigation satellite system sensors to understand how this vessel class behaves. This research is undertaken mainly in relation to fishing and recreational boats. With the advent of open-access satellite imagery and its ever-increasing resolution, it can support innovative methodologies that could eventually lead to the quantification of greenhouse gas emissions. Our work used deep learning algorithms to detect small boats in three cities in the Gulf of California in Mexico. The work produced a methodology named BoatNet that can detect, measure and classify small boats with leisure boats and fishing boats even under low-resolution and blurry satellite images, achieving an accuracy of 93.9% with a precision of 74.0%. Future work should focus on attributing a boat activity to fuel consumption and operational profile to estimate small boat greenhouse gas emissions in any given region

Best Practices in Stakeholder Engagement, Data Dissemination and Exploitation

Identification of successful stakeholder engagement mechanisms and tools to make available, disseminate and visualize ocean observations/data serving as guidance to AtlantO

Marine governance in the English Channel (La Manche): Linking science and management.

The English Channel is one of the world's busiest sea areas with intense shipping and port activity juxtaposed with recreation, communications and important conservation areas. Opportunities for marine renewable energy vie with existing activities for space. The current governance of the English Channel is reviewed and found to lack integration between countries, sectors, legislation and scientific research. Recent developments within the EU's marine management frameworks are significantly altering our approach to marine governance and this paper explores the implications of these new approaches to management of the English Channel. Existing mechanisms for cross-Channel science and potential benefits of an English Channel scale perspective are considered. In conclusion, current management practices are considered against the 12 Malawi Principles of the ecosystem approach resulting in proposals for enhancing governance of the region through science at the scale of the English Channel

Reliable Navigational Scene Perception for Autonomous Ships in Maritime Environment

Due to significant advances in robotics and transportation, research on autonomous ships has attracted considerable attention. The most critical task is to make the ships capable of accurately, reliably, and intelligently detecting their surroundings to achieve high levels of autonomy. Three deep learning-based models are constructed in this thesis to perform complex perceptual tasks such as identifying ships, analysing encounter situations, and recognising water surface objects. In this thesis, sensors, including the Automatic Identification System (AIS) and cameras, provide critical information for scene perception. Specifically, the AIS enables mid-range and long-range detection, assisting the decision-making system to take suitable and decisive action. A Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) is used to detect ships or objects. Following that, a Semi- Supervised Convolutional Encoder-Decoder Network (SCEDN) is developed to classify ship encounter situations and make a collision avoidance plan for the moving ships or objects. Additionally, cameras are used to detect short-range objects, a supplementary solution to ships or objects not equipped with an AIS. A Water Obstacle Detection Network based on Image Segmentation (WODIS) is developed to find potential threat targets. A series of quantifiable experiments have demonstrated that these models can provide reliable scene perception for autonomous ships

Ship Leadership, Situation Awareness, and Crew Safety Behaviour—Preregistered Replications in Two Survey Datasets

Situation awareness is often assumed to be crucial for working safely. Self-reported context-general measures can be an efficient way to measure situation awareness in large datasets and test how it relates to other individual, organizational, and environmental variables. In a previous structural equation model (Sætrevik & Hystad, 2017) authentic leadership accounted for situation awareness and self-report of committing unsafe actions, while situation awareness accounted for subjective risk assessment and commitment of unsafe actions. The current study performed preregistered replications of the same associations in two novel but similar datasets. Both datasets replicated that higher situation awareness was associated with fewer unsafe actions and with lower subjective risk assessment. One of the new datasets measured leadership, and more authentic leadership was found to be associated with higher situation awareness and fewer unsafe actions. The preregistered structural equation models explained large amounts of the variance in situation awareness and unsafe actions and medium to large amounts of the variance in subjective risk assessment. We also tested adjusted models that incorporated more of the measured items and improved the validity of the measures. The study supports the claim that a crewmember’s cognitive states (such as perception, understanding, and prediction of safety signals) are associated with safety outcomes and that leadership qualities may facilitate this relationship. This preregistered replication in two novel datasets increases the reliability of the previously identified relationships.publishedVersio