Search Results (99)

The Automatic Identification System (AIS) has become a key data source for ship behavior monitoring and maritime traffic management, widely used in trajectory prediction and anomaly detection. However, AIS data suffer from issues such as spatial sparsity, heterogeneous features, variable message formats, and irregular sampling intervals, while vessel trajectories are characterized by strong spatial–temporal dependencies. These factors pose significant challenges for efficient and accurate modeling. To address this issue, we propose a lightweight vessel trajectory prediction framework that integrates Spatial–Temporal Identity encoding with an MLP-Mixer architecture. The framework discretizes spatial and temporal features into structured IDs and uses dual MLP modules to model temporal dependencies and feature interactions without relying on convolution or attention mechanisms. Experiments on a large-scale real-world AIS dataset demonstrate that the proposed STID-Mixer achieves superior accuracy, training efficiency, and generalization capability compared to representative baseline models. The method offers a compact and deployable solution for large-scale maritime trajectory modeling. Full article

This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports real-time and distributed processing of hydrophone data collected in diverse aquatic environments. Acoustic signals captured by heterogeneous hydrophones—featuring varying sensitivity and bandwidth—are streamed to the cloud, where several machine learning algorithms can be deployed to extract distinguishing acoustic signatures from vessel engines and propellers in interaction with water. The architecture leverages cloud-based services for data ingestion, processing, and storage, facilitating robust vehicle detection and localization through propagation modeling and multi-array geometric configurations. Experimental validation demonstrates the system’s effectiveness in handling high-volume acoustic data streams while maintaining low-latency processing. The proposed approach highlights the potential of cloud technologies to deliver scalable, resilient, and adaptive acoustic sensing platforms for applications in maritime traffic monitoring, harbor security, and environmental surveillance. Full article

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article

This study presents a data-driven analysis of navigator stress and workload levels in simulated ship encounters within restricted waters, leveraging real-world automatic identification system (AIS) data from Makassar Port, Indonesia. Six close-quarter scenarios were recreated to reflect critical encounter geometries, and 24 Indonesian seafarers were evaluated using heart rate variability (HRV), perceived stress scale (PSS), and task load index (NASA-TLX) workload assessments. The results indicate that crossing angles, particularly 135° port and starboard encounters, significantly influence physiological stress levels, with age being a moderating factor. Although no consistent relationship was found between workload and HRV metrics, the findings underscore key human factors that may impair navigational performance under cognitively demanding conditions. By integrating AIS-derived traffic data with simulation-based human performance monitoring, this study supports the development of intelligent maritime training frameworks and adaptive decision support systems. The research contributes to broader efforts toward enhancing navigational safety and situational awareness amid increasing automation and traffic densities at sea. Full article

Ballast water has become a significant vector for the global spread of non-indigenous aquatic species. These species may cause severe ecological disruption and economic losses when introduced into new environments. Traditional monitoring techniques often lack the sensitivity and efficiency required for early monitoring, hindering timely and effective management. In this study, we used environmental DNA (eDNA) technology to assess fish diversity and identify non-indigenous fish species in ballast water samples collected from 14 international vessels entering Dongjiakou Port, China. Genetic evidence of five non-indigenous fish species was monitored, including two recognized invasive species (Lates calcarifer and Anguilla anguilla). Among all groups, samples from Group B (V2, V3, V6, V8) exhibited the highest diversity of non-indigenous species, suggesting regional differences in species composition that may reflect source port biodiversity. These findings highlight the utility of eDNA-based monitoring not only for early detection of potentially non-indigenous taxa but also for capturing biogeographic patterns associated with global maritime traffic. By demonstrating the effectiveness of this approach at an international port, this study contributes a scientific foundation for both local biodiversity conservation and broader ecological surveillance, offering valuable insights for the ongoing development of ballast water management strategies worldwide. Full article

Synthetic aperture radar (SAR) ship detection holds significant application value in maritime monitoring, marine traffic management, and safety maintenance. Despite remarkable advances in deep-learning-based detection methods, performance remains constrained by the vast size differences between ships, limited feature information of small targets, and complex environmental interference in SAR imagery. Although many studies have separately tackled small target identification and multi-scale detection in SAR imagery, integrated approaches that jointly address both challenges within a unified framework for SAR ship detection are still relatively scarce. This study presents MC-ASFF-ShipYOLO (Monte Carlo Attention—Adaptively Spatial Feature Fusion—ShipYOLO), a novel framework addressing both small target recognition and multi-scale ship detection challenges. Two key innovations distinguish our approach: (1) We introduce a Monte Carlo Attention (MCAttn) module into the backbone network that employs random sampling pooling operations to generate attention maps for feature map weighting, enhancing focus on small targets and improving their detection performance. (2) We add Adaptively Spatial Feature Fusion (ASFF) modules to the detection head that adaptively learn spatial fusion weights across feature layers and perform dynamic feature fusion, ensuring consistent ship representations across scales and mitigating feature conflicts, thereby enhancing multi-scale detection capability. Experiments are conducted on a newly constructed dataset combining HRSID and SSDD. Ablation experiment results demonstrate that, compared to the baseline, MC-ASFF-ShipYOLO achieves improvements of 1.39% in precision, 2.63% in recall, 2.28% in ${AP}_{50}$, and 3.04% in $AP$, indicating a significant enhancement in overall detection performance. Furthermore, comparative experiments show that our method outperforms mainstream models. Even under high-confidence thresholds, MC-ASFF-ShipYOLO is capable of predicting more high-quality detection boxes, offering a valuable solution for advancing SAR ship detection technology. Full article

Distributed Acoustic Sensing (DAS) technology presents an innovative method for marine monitoring by adapting existing underwater optical fiber networks. This paper examines the use of DAS with the Istituto Nazionale di Fisica Nucleare–Laboratori Nazionali del Sud (INFN-LNS) optical fiber infrastructure in the Gulf of Catania, Eastern Sicily, Italy. This region in the Western Ionian Sea provides a unique natural laboratory due to its tectonic and volcanic activity, proximity to Mount Etna, diverse marine ecosystems and significant human influence through maritime traffic. By connecting a 28 km long optical cable to an Alcatel Submarine Network OptoDAS interrogator, DAS successfully detected a range of natural and human–made signals, including a magnitude 3.5 ML earthquake recorded on 14 November 2023, and acoustic signatures from vessel noise. The earthquake–induced Power Spectral Density (PSD) increased to up to 30 dB above background levels in the 1–15 Hz frequency range, while vessel noise exhibited PSD peaks between 30 and 60 Hz with increases of up to 5 dB. These observations offered a detailed spatial and temporal resolution for monitoring seismic wave propagation and vessel acoustic noise. The results underscore DAS’s capability as a robust tool for the continuous monitoring of the rich underwater environments in the Gulf of Catania. Full article

The remote monitoring of maritime signaling facilities is one of the marine navigation safety rules essential for ensuring global maritime traffic. Some maritime signaling facilities have not yet implemented remote monitoring systems. This challenge is posed by factors such as insufficient signal range, limited availability of electrical energy, or various economic reasons. Therefore, this paper reviews the current and relevant scientific literature on 10 communication technologies for maritime signaling facilities in the last two decades using PRISMA guidelines. PRISMA 2020 represents guidelines for conducting systematic review papers using mixed methods, including their applicability to various reviews. In addition, this paper analyzes the selection of the best-suited communication technology for communication between maritime signaling facilities. The results show that, initially, 214 papers met the specified criteria, and after applying the filtering, it was narrowed to 29 relevant papers for the research topic. Surprisingly, almost half of them were found in databases other than WOS, SCOPUS, and GOOGLE SCHOLAR. Also, LoRa WAN is the most energy-efficient and cost-effective option, with a consumption rate 2.14 times lower than AIS and NB-IoT. To summarize, it has been found that LoRa WAN represents the optimal communication technology for transmitting data from maritime signaling facilities across long distances. Full article

This study examines the impact of monitoring, reporting, and verification (MRV) system indicators on the costs associated with the emissions trading system (ETS) of the maritime sector in the European Union. Since maritime transport has recently been incorporated into the ETS, it becomes essential to understand how different operational and environmental factors affect the economic burden of shipping companies and port competitiveness. To this end, a model based on Bayesian networks is used to analyse the interdependencies between key variables, facilitating the identification of the most influential factors in the determination of the costs of the ETS. The results show that fuel efficiency and CO₂ emissions in port are decisive in the configuration of costs. In particular, it was identified that emissions during the stay in port have a greater weight than expected, which suggests that strategies such as the use of electrical connections in port (cold ironing) may be key to mitigating costs. Likewise, navigation patterns and traffic regionalisation show a strong correlation with ETS exposure, which could lead to adjustments in maritime routes. This probabilistic model offers a valuable tool for strategic decision-making in the maritime sector, benefiting shipping companies, port operators, and policymakers. However, future research could integrate new technologies and regulatory scenarios to improve the accuracy of the analysis and anticipate changes in the ETS cost structure. Full article

Small target ship detection and anomaly analysis play a pivotal role in ocean remote sensing technologies, offering critical capabilities for maritime surveillance, enhancing maritime safety, and improving traffic management. However, existing methodologies in the field of detection are predominantly based on deep learning models with complex network architectures, which may fail to accurately detect smaller targets. In the classification domain, most studies focus on synthetic aperture radar (SAR) images combined with Automatic Identification System (AIS) data, but these approaches have significant limitations: first, they often overlook further analysis of anomalies arising from mismatched data; second, there is a lack of research on small target ship classification using wide-area optical remote sensing imagery. In this paper, we develop SVIADF, a multi-source information fusion framework for small vessel identification and anomaly detection. The framework consists of two main steps: detection and classification. To address challenges in the detection domain, we introduce the YOLOv8x-CA-CFAR framework. In this approach, YOLOv8x is first utilized to detect suspicious objects and generate image patches, which are then subjected to secondary analysis using CA-CFAR. Experimental results demonstrate that this method achieves improvements in Recall and F1-score by 2.9% and 1.13%, respectively, compared to using YOLOv8x alone. By integrating structural and pixel-based approaches, this method effectively mitigates the limitations of traditional deep learning techniques in small target detection, providing more practical and reliable support for real-time maritime monitoring and situational assessment. In the classification domain, this study addresses two critical challenges. First, it investigates and resolves anomalies arising from mismatched data. Second, it introduces an unsupervised domain adaptation model, Multi-CDT, for heterogeneous multi-source data. This model effectively transfers knowledge from SAR–AIS data to optical remote sensing imagery, thereby enabling the development of a small target ship classification model tailored for optical imagery. Experimental results reveal that, compared to the CDTrans method, Multi-CDT not only retains a broader range of classification categories but also improves target domain accuracy by 0.32%. The model extracts more discriminative and robust features, making it well suited for complex and dynamic real-world scenarios. This study offers a novel perspective for future research on domain adaptation and its application in maritime scenarios. Full article

The maritime traffic status is monitored through the Automatic Identification System (AIS) installed on vessels. AIS data record the trajectory of each ship. However, due to the short sampling interval of AIS data, there is a significant amount of redundant data, which increases storage space and reduces data processing efficiency. To reduce the redundancy within AIS data, a compression algorithm is necessary to eliminate superfluous points. This paper presents an offline trajectory compression algorithm that leverages geospatial background knowledge. The algorithm employs an adaptive function to preserve points characterized by the highest positional errors and rates of water depth change. It segments trajectories according to their distance from the shoreline, applies varying water depth change rate thresholds depending on geographical location, and determines an optimal distance threshold using the average compression ratio score. To verify the effectiveness of the algorithm, this paper compares it with other algorithms. At the same compression ratio, the proposed algorithm reduces the average water depth error by approximately 99.1% compared to the Douglas–Peucker (DP) algorithm, while also addressing the common problem of compressed trajectories potentially intersecting with obstacles in traditional trajectory compression methods. Full article

Mapping anthropic activities in aquatic environments is crucial to support their sustainable management. Aquatic traffic is one of the human-related activities gaining relevance nowadays, and remote sensing can support the description of the distribution of vessels, particularly small boats or other vessels not tracked with other tools. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we performed a systematic review of the literature to describe current trends, key methodologies, and gaps, with special regard to the challenges of monitoring small boats that are not equipped with Global Positioning System (GPS) transponders. A total of 133 studies published between 1992 and 2024 were included. The research effort is mainly dedicated to developing new methods or upgrading existing ones, with only a few studies focusing on applications in a contest of environmental studies and, among these, only a few focusing on small boats. To promote the use of remote sensing by environmental scientists, coastal, and fishery managers, explicative case studies are delineated, showing how boat identification through satellites can support environmental studies. Moreover, a guideline section for using remote sensing to integrate monitoring of small boats is given to promote newcomers to this field. Full article

The Mediterranean Sea, a biodiversity hotspot, faces significant threats from non-indigenous species (NIS), which drive biodiversity changes. Over the past century, the introduction of NIS has accelerated due to maritime traffic, aquaculture, and interoceanic canals, fostering biological invasions. Marine protected areas (MPAs), established to preserve biodiversity, are increasingly impacted. This review quantified and characterized French Mediterranean MPAs, analyzing non-indigenous macroalgae distribution based on the existing literature and the authors’ observations. Results revealed widespread occurrence, with the highest NIS richness in strictly regulated MPAs; their proximity to large harbors highlights the paramount importance of the introduction pathways. In addition, there is a significant knowledge gap regarding the distribution of NIS within MPAs, complicating efforts to monitor and study these species effectively. These findings highlight the challenges in monitoring and managing invasions and the urgent need for controlling primary and secondary invasion pathways, within and outside the MPAs, international collaboration to control them, and enhanced funding for NIS monitoring. Without adaptive management, even strictly protected MPAs are vulnerable to the escalating impacts of invasive species. Full article

Ship orientation detection is essential for maritime navigation, traffic monitoring, and defense, yet existing methods face challenges with rotational invariance in large-angle scenarios, difficulties in multi-scale feature fusion, and the limitations of traditional IoU when detecting oriented objects and predicting objects’ orientation. In this article, we propose a novel ship orientation detection (RACR-ShipDet) network based on rotation-adaptive ConvNeXt and Enhanced RepBiFPAN in remote sensing images. To equip the model with rotational invariance, ConvNeXt is first improved so that it can dynamically adjust the rotation angle and convolution kernel through adaptive rotation convolution, namely, ARRConv, forming a new architecture called RotConvNeXt. Subsequently, the RepBiFPAN, enhanced with the Weighted Feature Aggregation module, is employed to prioritize informative features by dynamically assigning adaptive weights, effectively reducing the influence of redundant or irrelevant features and improving feature representation. Moreover, a more stable version of KFIoU is proposed, named SCKFIoU, which improves the accuracy and stability of overlap calculation by introducing a small perturbation term and utilizing Cholesky decomposition for efficient matrix inversion and determinant calculation. Evaluations using the DOTA-ORShip dataset demonstrate that RACR-ShipDet outperforms current state-of-the-art models, achieving an mAP of 95.3%, representing an improvement of 5.3% over PSC (90.0%) and of 1.9% over HDDet (93.4%). Furthermore, it demonstrates a superior orientation accuracy of 96.9%, exceeding HDDet by a margin of 5.0%, establishing itself as a robust solution for ship orientation detection in complex environments. Full article

The development of China’s digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data. Full article