Generating aerial shoreline segmentation masks can be a daunting task, often requiring manual labeling or correction. This is further problematic because neural segmentation models require decent and abundant data for training, requiring even more manpower to automate the process. In this paper, we propose utilizing Unmanned Surface Vehicles (USVs) in an automated shoreline segmentation system on satellite imagery. The remotely controlled vessel first collects above- and underwater shoreline information using light detection and ranging (LiDAR) and multibeam echosounder (MBES) measuring instruments, resulting in a geo-referenced 3D point cloud. After cleaning and processing these data, the system integrates the projected map with an aerial image of the region. Based on the height values of the mapped points, the image is segmented. Finally, post-processing methods and the k-NN algorithm are introduced, resulting in a complete binary shoreline segmentation mask. The obtained data were used for training U-Net-type segmentation models with pre-trained backbones. The InceptionV3-based model achieved an accuracy of 96% and a dice coefficient score of 93%, demonstrating the effectiveness of the proposed system as a source of data acquisition for training deep neural networks.

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel.

The paper presents collaborative research conducted by European researchers, mariners, and cybersecurity experts on identifying and developing preventive procedures for maritime cybersecurity within the CyberSEA project. The project's objective is to equip seafarers with the necessary knowledge to detect and respond effectively to cyber threats, minimizing the risk of disruptions to maritime operations. The research included several key tasks aimed at developing the proposed cybersecurity protocols. The initial phase involved identifying cyber vulnerabilities in the maritime sector and defining the main risk areas. Subsequently, 34 real-life scenarios were constructed to illustrate these threats. Comprehensive desk research was conducted on best cybersecurity practices in other industries that were transferable to the maritime sector to establish a set of preventive measures. The final phase resulted in the development of 10 detailed protocols for seafarers, specifying the procedural steps to be taken in response to distinct cyber threats. The study highlights the essential role of human factors and education in maritime cyber resilience, offering a transferable framework for integration into Safety Management Systems and vocational training curricula.

This paper presents the challenges encountered in the dimensional control of ships, platforms, and offshore units. This novel approach utilizes machine learning (MLP—Multilayer Perceptron Neural Network) for three-dimensional (3D) spatial coordinate transformations when only three common points are known. The proposed method was verified based on laboratory and field data. The main issue was to provide a sufficient number of valid training points. The oversampling method was used to meet this criterion. The achieved results indicate equal or better accuracy when the points were located inside the adjustment points array. In the case where the points lay outside this array, no improvement in the accuracy of the transformation was observed. The neural approach restores the transformation symmetry, and in some cases, such as the study of deformation of engineering objects, breaks the symmetry rather than restoring it.

This article is an introduction to the DESSEV (DEcision Support System regarding the risk of Epidemic threats on a sea-going Vessel) project implemented as part of the Erasmus+ partnership under the leadership of the Maritime University of Szczecin. This project consists of three elements: a data repository, a rule base and a decision support system. Due to the schedule of works in the project, only the first of them was presented. The next ones will be described in subsequent publications at various international conferences. The idea of the project naturally resulted from the COVID-19 pandemic, but other infectious outbreaks may also occur on sea vessels, posing a threat to the ship's crew and passengers (passenger transport). The existing legal regulations, available knowledge or training do not sufficiently address the problem of the epidemic on the state. Therefore, it was decided to introduce a novelty - a decision support system, which is designed to facilitate taking the right steps in the event of an infectious disease on a seagoing vessel.

Epidemic outbreaks on sea-going vessels pose a significant health and safety risk, particularly in isolated maritime environments lacking professional medical staff. The COVID-19 pandemic exposed major deficiencies in the preparedness and response protocols of maritime operations, where vessels became de facto quarantine units without access to structured medical decision support. This study presents the development and evaluation of DESSEV, a decision support system designed to assist maritime personnel in identifying and managing infectious disease outbreaks onboard vessels. A comprehensive non-SQL knowledge base was constructed using international epidemiological guidelines (WHO, CDC, ECDC) and medical literature, encompassing 22 infectious diseases and 35 symptoms categorized into 8 clinical domains. To support probabilistic diagnostics, artificial patient profiles were generated reflecting real-world symptom distributions. Three predictive models—Decision Tree, Naive Bayes, and Random Forest were trained. Their performances were assessed through cross-validation and random sampling techniques. Evaluation on a holdout test set of real patient cases showed that the Random Forest model achieved superior performance across all The Random Forest algorithm was thus selected as the core prediction engine for the DESSEV application. While not a substitute for professional medical care, DESSEV improves situational awareness and supports early risk mitigation actions aboard vessels. Future work will focus on integrating real-time health telemetry and user feedback to further refine diagnostic accuracy and usability.