Critical maritime events present substantial social, environmental, and economic risks, particularly as climate change increases the frequency and severity of hazardous weather along major maritime trade routes. This paper presents a weather-aware vessel rerouting framework that integrates real-time meteorological forecasts with large-scale historical vessel traffic patterns from the Kpler MarineTraffic Platform observed under diverse sea conditions, aiming to enhance navigational safety during extreme weather events while maintaining maritime operational efficiency. The core of the proposed approach is a modified A* search algorithm, where edge weights are dynamically assigned based on forecasted weather severity and historical vessel trip density, as a function of the prevailing sea state. The approach is evaluated using randomly selected origin-destination pairs across the southeastern U.S. Coast in September 2022, a period when a range of weather scenarios in terms of severity, spatial extent, and duration. Results indicate a 16.97% reduction in median cumulative weather penalties in variable sea conditions and a 35.03% decrease very extreme sea conditions when compared to shortest-path routing. A case study for Hurricane Fiona (Category 4, 21 September 2022) further demonstrates the system's ability to entirely avoid areas forecasted to experience extreme sea conditions. Findings highlight the value of integrating AIS-based collective fleet intelligence with weather data from historical databases to improve voyage planning and vessel operations in dynamically changing sea conditions.

The ship trajectories collected by the Automatic Identification System (AIS) are widely used in maritime applications. However, a significant issue with AIS data is that large AIS gaps occur. Existing trajectory imputation methods for AIS data have three main limitations: (1) the temporal aspect is ignored; (2) the methods fall short when dealing with complex ship movements; (3) the common-route assumption does not always hold. To overcome these limitations, we propose TrajImpMC, a tracking-based framework that uses polygon-based ship location estimates from multiple cameras to impute large AIS gaps. TrajImpMC combines speed constraints and Kalman filters, and can return imputed trajectories that contain both spatial and temporal information. Extensive experiments are conducted on real datasets. In terms of the quality of the imputed trajectories, TrajImpMC improves the RMSE errors by at least one order of magnitude over two existing state-of-the-art AIS imputation methods. In addition, a visual comparison shows that the imputed trajectories of TrajImpMC align very well with the real ship trajectories during AIS gaps. The code for this paper is available at: https://github.com/songwu0001/TrajImpMC.

Recent advances, especially in deep learning, allow to effectively detect ship targets in surveillance videos. However, the translation of these detections to the real-world locations of ships has not been sufficiently explored. The common approach in the literature is using a transformation matrix to convert a pixel to a real-world coordinate. However, this approach has three shortcomings: first, a set of reference point pairs has to be manually prepared to establish the matrix; second, the matrix always maps a pixel to the same real-world coordinate, ignoring that there is no one-to-one correspondence between discrete pixel coordinates and continuous real-world coordinates; third, this approach can only work with one camera. In light of this, we propose a technique PixelToRegion that explicitly takes into account the uncertainty in coordinate conversion by mapping each pixel to a spatial polygon. Next, we propose a new algorithm MCbSLE that can estimate ship locations using pixel sets from multiple cameras. The precision of location estimation by MCbSLE is enhanced through spatial intersection between polygons from different cameras. Experiments are conducted under 16 carefully designed multi-camera settings to evaluate MCbSLE w.r.t. four factors: different ports, the number of cameras, the distance between cameras, and camera headings. Results on one-day ship trajectory data show that (1) an 79.8% accuracy in the number of coordinates can be achieved by MCbSLE when there are no more than 10 ships in camera views; (2) using multiple cameras can improve the precision of location estimation by one order of magnitude compared with using one camera.

Monitoring vessel traffic on a global scale is a complex and challenging task. The large number of moving vessels and the complexity of monitoring their position and forecasting their route in real-time require novel, advanced and highly scalable big-data mechanisms. In this work a digital twin for constant maritime situational awareness on a global scale is presented. The described multi-layered system is able to visualize maritime traffic in real-time, based on data from the Automatic Identification System (AIS), while also providing forecasts of future movement based on machine learning and deep learning techniques. The system is validated using real streaming AIS data from around the globe to demonstrate its performance, scalability and parallelization efficiency.

Complex Event Processing (CEP) 's main purpose is recognizing interesting phenomena upon streams of data. So its only natural that it would find applications in the maritime domain, where detecting vessel activity plays an important role in monitoring movement at sea. In this study we briefly examine the field of Complex Event Processing; we present two CEP implementations, one based on machine learning techniques and a rule-based system modeled with Event Calculus. Finally, we evaluate their ability in modeling activities that involve multiple vessels, by comparing their results on real-life examples.