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Compared to structured ocean environments, unstructured ocean environments are inherently more complex. In such unstructured environments, the presence of narrow waterways poses unique navigational hurdles for autonomous surface vehicles (ASVs) due to their restricted connectivity. Current path planning algorithms designed for unstructured environments, particularly those characterized by narrow spaces, often face difficulties in efficiently exploring the target area while producing high-quality paths. In this study, we tackle the aforementioned complexities by incorporating progressive sampling and point cloud clustering, which jointly expedite the detection of constrained waterways in unstructured marine environments. More specifically, we generate multiple random trees from these sampling points, thereby bolstering both navigational accuracy and overall computational efficiency. Building upon these core techniques, we introduce a novel extension of the traditional rapidly-exploring random trees (RRT) connect algorithm-referred to as multiple RRT-connect (multi-RRT-connect)-aimed at swiftly determining a viable path between prescribed start and goal coordinates. As the number of samples expands, the random trees gradually enlarge and interlink, mirroring the functionality of classic RRT-connect and ultimately forming a continuous corridor. Subsequently, the derived path undergoes iterative refinement and optimization, culminating in a significantly reduced trajectory length.We subjected the proposed algorithm to rigorous testing through comprehensive simulations alongside meticulous comparisons with established state-of-the-art solutions. The results highlight the algorithm's distinct advantages across multiple dimensions such as path construction success, computational efficiency, and trajectory refinement quality, thereby underscoring its potential to advance autonomous navigation in challenging maritime settings.

Large-scale weather forecasting is critical for ensuring maritime safety and optimizing transoceanic voyages. However, sparse meteorological data, incomplete forecasts, and unreliable communication hinder accurate, high-resolution wind system predictions. This study addresses these challenges to enhance dynamic voyage planning and intelligent ship navigation. We propose IPCA-MHA-DSRU-Net, a novel deep learning model integrating incremental principal component analysis (IPCA) with a spatial-temporal depthwise separable U-Net. Key components include: (1) IPCA preprocessing to reduce dimensionality and noise in 2D wind field data; (2) depthwise-separable convolution (DSC) blocks to minimize parameters and computational costs; (3) multi-head attention (MHA) and residual mechanisms to improve spatial-temporal feature extraction and prediction accuracy. The framework is optimized for real-time onboard deployment under communication constraints. The model achieves high accuracy in high-resolution wind predictions, validated through reanalysis datasets. Experiments demonstrated enhanced path planning efficiency and robustness in dynamic oceanic conditions. The IPCA-MHA-DSRU-Net balances computational efficiency and accuracy, making it viable for resource-limited ships. This novel IPCA application provides a promising alternative for preprocessing large-scale meteorological data.

The international iceberg patrol service set up after the sinking of the Titanic is now able to track drifting ice from orbit more swiftly through ESA-backed cloud computing.

The main target of DSP in respect of Contship Italia Group, is to address the IT strategies of the various companies to guarantee an harmonious development respecting the peculiarity and the autonomy of the single realities. In the last years DSP, using the acquired know-how in the IT problems of shipping, port management and intermodal transportation has enlarged is portfolio of activities offering to the market professional services in terminal operations processes and systems deployment and optimisiation. In 2007 DSP became partner of NAVIS (part of Cargotec Corporation) and certified its staff as SPARCS 3.7 and SPARCS N4 senior consultants, carrying out various international projects. DSP has recently developed for Contship Italia Group an innovative and flexible system for automatic invoicing (Fatteuro) for container and general cargo terminals interfaced with other systems in order to manage all the necessary information to calculate and register the invoices. It is currently is use at CICT (Cagliari), EGT(Tangier), LSCT(La Spezia) and at the General Cargo Terminal SPETER of La Spezia. DSP is also tightly linked with the new University of Applied Science of Southern Switzerland. In his team a professor of this university is leading analysis and design activities and most part of his personnel has a degree in Computer Science and where recruited there. This also gives the chance to DSP to participate to research projects on the transport and IT area and to remain always skilled with the newest technology

A warming planet means rising oceans, but seaports are not prepared for the expensive construction they will need to protect themselves, according a global survey of ports conducted by Stanford researchers. But the researchers have created a computer model that will help ports with their planning.

Israeli navy veteran Ami DANIEL points at his computer screen and explains why the ship he was tracking should have been stopped and searched.

Drowning accidents followed by the disappearance of the body are particularly distressing events. When such tragedy strikes, Search and Rescue Operations are usually deployed to recover the body. The efficiency of such efforts can be enhanced by timely data and appropriate data integration tools, such as operational prediction systems relying on numerical models or other data sources. In this paper, we propose four stages for Search and Rescue Operations after drowning accidents and briefly address the critical role of ocean observations at each stage, as well as the relevancy of available computational resources. The potential of the combination of different data sources on the state of the sea to provide better insights is discussed. This work encourages oceanographers, data scientists and relevant marine stakeholders to produce knowledge and tools to support Search and Rescue Operations after drowning accidents.

The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union's incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port-a region characterized by high shipping activity-this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under vari

Ground-breaking marine perception sensor provides high-definition situational awareness to eliminate at-sea collisions and allisions, and increase operational performance​​​​.

Sandia scientists develop computer tools to study inadvertent marine cloud brightening.

The developed computational fluid dynamics model can lead to more accurate predictions of path-following performance.

Introduction: High-resolution sonar systems are critical for underwater robots to obtain precise environmental perception. However, the computational demands of processing sonar imagery in real-time pose significant challenges for autonomous underwater vehicles (AUVs) operating in dynamic environments. Current segmentation methods often struggle to balance processing speed with accuracy.