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The rapid development of maritime transport and the gradual increase in the number of ports, ships and shipping routes can produce direct economic benefits for regional development, and a profound grasp of the actual situation of maritime transport makes it possible to make practical plans for economic development and thus rationally develop and use marine resources. However, there is a lack of research on maritime transport, so this study is based on the AIS, waterways, routes and ports data in the near-shore sea area of Guangdong Province. Using GIS spatial analysis technology and various mathematical models, we refer to the evaluation method of road traffic dominance. It analyses and proposes the evaluation indexes of maritime traffic advantages, such as the density of maritime traffic network and the proximity of ports. Based on the actual situation of the study regions, the indicators were quantitatively evaluated and analyzed. The economic development level of each region was also quantitatively evaluated using principal component analysis, and the study regions were classified based on the coupling-coordination model. The results show that the coastal area of Guangdong Province is divided into four types of zones according to the coupling-coordination type of zoning criteria. Corresponding development suggestions are put forward for different zones, and the research results provide certain practical guidelines for promoting the benign cycle development of maritime traffic and economy, and have important guiding significance and application value for the organization and safety of maritime traffic. [ABSTRACT FROM AUTHOR]

The day and night band (DNB) of a visible infrared imaging radiometer suite (VIIRS) sensor is designed to detect both natural and artificial visible light during nighttime observations. Thus, this sensor can be used to detect night fishing activity around the Korean Peninsula. A comparative analysis of unmatched vessels detected using the VIIRS and automatic identification system (AIS) can help in understanding the static and dynamic characteristics of vessel distribution. In this study, we aimed to analyze the annual density distribution of nighttime fishing vessels by matching the location of VIIRS-derived vessels with that obtained from AIS and V-Pass (a fishing vessel location transmitter) data. The annual VIIRS data revealed that the vessel density showed a seasonal trend according to the fishing ground. During the peak fishing season, the vessel lights were concentrated at boundaries of the exclusive fishery zone. VIIRS data that were unmatched with the AIS/V-Pass data revealed that the vessels were mainly distributed outside the exclusive fishery zone. In contrast, the unmatched AIS/V-Pass data showed that the vessels were concentrated in the coastal areas of Korea, because low-light vessels undetectable by VIIRS were dominant in the inshore area. Two density hotspots of vessels were observed outside the exclusive fishery zone in the southwestern sea of Jeju Island, where unmatched VIIRS and AIS/V-Pass data were recorded. These findings will contribute to the identification of trends in fishing fleets around the Korean Peninsula and the improvement of maritime domain awareness. [ABSTRACT FROM AUTHOR]

Ionospheres over sea areas have an inevitable impact on maritime–satellite communications; however, due to geographic constraints, ionospheric observation and analysis over sea areas are far from adequate. In our paper, slant total electron content (STEC) along small-satellite constellation–automatic identification system (AIS) signal rays is used for computerized ionospheric tomography (CIT) over sea areas, and small-satellite constellations can provide more effective signal rays than a single satellite. An adjustment factor δ is introduced to optimize the initial electron density for the multiplicative algebraic reconstruction technique (MART). The CIT results reconstructed by a traditional MART and our new method at 00:00 and 06:00, 15 March 2022, are compared, and our new method produces about a 15% and over 40% improvement in average deviation (AD) and root-mean-square error (RMSE). The results show that the bigger the difference between δ and 1, the better improvement will be in the 3D CIT process. The initial electron density is well selected during CIT when δ is approximate to 1, which is the case at 12:00, and the reconstructed 3D electron density, applying the initial ne and the adjusted initial ne, are both close to the true electron density. The small-satellite constellation–AIS signals are valuable resources for electron density reconstruction in sea areas. [ABSTRACT FROM AUTHOR]

Abstract Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. However, in the case of Automated Information Systems (AIS), attached to vessels, observed strange behaviors in the tracking datasets may come from intentional manipulation of the electronic devices. Thus, the analysis of anomalies can provide valuable information on suspicious behavior. Here, we analyze anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silent anomalies, those that occur when positioning data are absent for more than 24 hours, shows that they are most likely to occur closer to land, with 87.1% of anomalies observed within 100 km of the coast. This behavior suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimize the management of fishing resources.

Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.

The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only provides positional and morphological characteristics without detailed identity information, presenting limitations as a sole data source. To address this issue, this paper proposes a method for enhancing maritime target identification and positioning accuracy through the fusion of Automatic Identification System (AIS) data and satellite remote-sensing imagery. The AIS utilizes radio communication to acquire multidimensional feature information describing targets, serving as an auxiliary data source to complement the limitations of image data and achieve maritime target identification. Additionally, the positional information provided by the AIS can serve as maritime control points to correct positioning errors and enhance accuracy. By utilizing data from the Jilin-1 Spectral-01 satellite imagery with a resolution of 5 m and AIS data, the feasibility of the proposed method is validated through experiments. Following preprocessing, maritime target fusion is achieved using a point-set matching algorithm based on positional features and a fuzzy comprehensive decision method incorporating attribute features. Subsequently, the successful fusion of target points is utilized for positioning error correction. Experimental results demonstrate a significant improvement in maritime target positioning accuracy compared to raw data, with over a 70% reduction in root mean square error and positioning errors controlled within 4 pixels, providing relatively accurate target positions that essentially meet practical requirements. [ABSTRACT FROM AUTHOR]

Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. However, in the case of Automated Information Systems (AIS), attached to vessels, observed strange behaviors in the tracking datasets may come from intentional manipulation of the electronic devices. Thus, the analysis of anomalies can provide valuable information on suspicious behavior. Here, we analyze anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silent anomalies, those that occur when positioning data are absent for more than 24 hours, shows that they are most likely to occur closer to land, with 87.1% of anomalies observed within 100 km of the coast. This behavior suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimize the management of fishing resources. [ABSTRACT FROM AUTHOR]

This study proposes the use of satellite images and a vessel's automatic identification system (AIS) data to evaluate the congestion level at container ports for operational efficiency analysis, which was never attempted in previous studies. The congestion level in container yards is classified by developing a convolutional neural network (CNN) model and an annotation tool to reduce the workload of creating training data. The annotation tool calculates the number of vertically stacked containers and the reliability of each container cell in a detection area by focusing on the shadows generated by the containers. Subsequently, a high-accuracy CNN model is developed for end-to-end processing to predict congestion levels. Finally, as an example of dynamic efficiency analysis of container terminals using satellite images, the relationship of the estimated average number of vertically stacked containers in the yard with the elapsed time between the image capture time and vessel arrival or departure time obtained from the automatic identification system data is analyzed. This study contributes to representing a prototype for dynamically estimating the number of vertically stacked containers and congestion level of container terminals using satellite images without statistical information, as well as its relationship with the timing of vessel arrival acquired from AIS data. [ABSTRACT FROM AUTHOR]

Efficient night-time vessel detection is of significant importance for maritime traffic management, fishery activity monitoring, and environmental protection. With the advancement in object-detection approaches, the method of night-time vessel detection has gradually shifted from traditional threshold segmentation to deep learning that balances efficiency and accuracy. However, the restricted spatial resolution of night-time light (NTL) remote sensing data (e.g., VIIRS/DNB images) results in fewer discernible features and insufficient training performance when detecting vessels that are considered small targets. To address this, we establish an Enhanced Dense Nested-Attention Network (DNA-net) to improve the detection of small vessel targets under low-light conditions. This approach effectively integrates the original VIIRS/DNB, spike median index (SMI), and spike height index (SHI) images to maintain deep-level features and enhance feature extraction. On this basis, we performed vessel detection based on the Enhanced DNA-net using VIIRS/DNB images of the Japan Sea, the South China Sea, and the Java Sea. It is noteworthy that the VIIRS Boat Detection (VBD) observations and the Automatic Identification System (AIS) data were cross-matched as the actual status of the vessels (VBD-AIS). The results show that the proposed Enhanced DNA-net achieves significant improvements in the evaluation metrics (e.g., IOU, Pd, Fa, and MPD) compared to the original DNA-net, achieving performance of 87.81%, 96.72%, 5.42%, and 0.36 Wpx, respectively. Meanwhile, we validated the detection performance of Enhanced DNA-net and strong VBD detection against VBD-AIS, showing that the Enhanced DNA-net achieves 1% better accuracy than strong VBD detection. [ABSTRACT FROM AUTHOR]

Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services. [ABSTRACT FROM AUTHOR]

The ocean, which occupies approximately 70% of the Earth’s surface, is where numerous ships navigate. Most maritime countries use various information systems for the systematic management of the maritime domain. The use of satellites to monitor the oceans is expanding, because they can acquire periodic images over wide areas. Particularly high-resolution satellites—such as the Korea Multipurpose Satellite (KOMPSAT) series—can effectively detect and identify various seagoing ships. In this study, ship detection and classification were performed using high-resolution optical and synthetic aperture radar (SAR) images from the KOMPSAT. To utilize deep learning (DL) technology which has recently been widely used in image processing, the generation of high-quality training data should be prioritized. Therefore, training data were produced for each ship type by combining automatic identification system (AIS) and fishing ship positioning system (V-Pass) information to allow for the differentiation between ship types in KOMPSAT images. In addition, a labeling tool was developed to increase the effectiveness of such training data generation. Subsequently, various DL models were applied to detect and classify ship targets in the KOMPSAT optical and SAR images. These models were developed as quantum geographic information system (QGIS) plugin modules, facilitating ship detection and classification via the QGIS platform and allowing a visualization of results. This paper presents the results of ship detection and classification based on DL models using KOMPSAT optical and SAR imagery. [ABSTRACT FROM AUTHOR]

Satellite-derived Sea Surface Temperature (SST) and sea-surface Chlorophyll a concentration (Chl-a), along with Automatic Identification System (AIS) data of fishing vessels, were used in the examination of the correlation between fishing operations and oceanographic factors within the northern Indian Ocean from March 2020 to February 2023. Frequency analysis and the empirical cumulative distribution function (ECDF) were used to calculate the optimum ranges of two oceanographic factors for fishing operations. The results revealed a substantial influence of the northeast and southwest monsoons significantly impacting fishing operations in the northern Indian Ocean, with extensive and active operations during the period from October to March and a notable reduction from April to September. Spatially, fishing vessels were mainly concentrated between 20° N and 6° S, extending from west of 90° E to the eastern coast of Africa. Observable seasonal variations in the distribution of fishing vessels were observed in the central and southeastern Arabian Sea, along with its adjacent high sea of the Indian Ocean. Concerning the marine environment, it was observed that during the northeast monsoon, the suitable SST contributed to high CPUEs in fishing operation areas. Fishing vessels were widely distributed in the areas with both mid-range and low-range Chl-a concentrations, with a small part distributed in high-concentration areas. Moreover, the monthly numbers of fishing vessels showed seasonal fluctuations between March 2020 and February 2023, displaying a periodic pattern with an overall increasing trend. The total number of fishing vessels decreased due to the impact of the COVID-19 pandemic in 2020, but this was followed by a gradual recovery in the subsequent two years. For fishing operations in the northern Indian Ocean, the optimum ranges for SST and Chl-a concentration were 27.96 to 29.47 °C and 0.03 to 1.81 mg/m3, respectively. The preliminary findings of this study revealed the spatial–temporal distribution characteristics of fishing vessels in the northern Indian Ocean and the suitable ranges of SST and Chl-a concentration for fishing operations. These results can serve as theoretical references for the production and resource management of off-shore fishing operations in the northern Indian Ocean. [ABSTRACT FROM AUTHOR]

With the advancement of satellite and 5G communication technologies, vehicles can transmit and exchange data from anywhere in the world. It has resulted in the generation of massive spatial trajectories, particularly from the Automatic Identification System (AIS) for surface vehicles. The massive AIS data lead to high storage requirements and computing costs, as well as low data transmission efficiency. These challenges highlight the critical importance of vessel trajectory compression for surface vehicles. However, the complexity and diversity of vessel trajectories and behaviors make trajectory compression imperative and challenging in maritime applications. Therefore, trajectory compression has been one of the hot spots in research on trajectory data mining. The major purpose of this work is to provide a comprehensive reference source for beginners involved in vessel trajectory compression. The current trajectory compression methods could be broadly divided into two types, batch (offline) and online modes. The principles and pseudo-codes of these methods will be provided and discussed in detail. In addition, compressive experiments on several publicly available data sets have been implemented to evaluate the batch and online compression methods in terms of computation time, compression ratio, trajectory similarity, and trajectory length loss rate. Finally, we develop a flexible and open software, called AISCompress, for AIS-based batch and online vessel trajectory compression. The conclusions and associated future works are also given to inspire future applications in vessel trajectory compression.

Satellite imagery has become a fundamental part for maritime monitoring and safety. Correctly estimating a ship's identity is a vital tool. We present a method based on facial recognition for identifying ships in satellite images. A large ship dataset is constructed from Sentinel-2 multispectral images and annotated by matching to the automatic identification system. Our dataset contains 7000 unique ships, for which a total of 16 000 images are acquired.The method uses a convolutional neural network to extract a feature vector from the ship images and embed it on a hypersphere. Distances between ships can then be calculated via the embedding vectors. The network is trained using a triplet loss function, such that minimum distances are achieved for identical ships and maximum distances to different ships. Comparing a ship image to a reference set of ship images yields a set of distances. Ranking the distances provides a list of the most similar ships. The method correctly identifies a ship on average 60% of the time as the first in the list. Larger ships are easier to identify than small ships, where the image resolution is a limitation.

The presence of artificial reefs (ARs) in the south of Portugal that were deployed a few decades ago and the corroboration of fishing patterns and other activities related to the use of these habitats have not been followed. It is important to note that monitoring the use of ARs was difficult in the past but is currently facilitated by the application of non-intrusive tools. In the present study, an approach is developed where, based on monitoring data from fishing and non-fishing boats, influence diagrams (IDs) are constructed to provide some evidence on fisheries or other use patterns and consequent AR effectiveness as coastal tools. These IDs allow us to infer various usefulness scenarios, namely catches, which are tangible, and satisfaction, which is intangible, and overall assessment of ARs and nearby areas in terms of human activities. After calibrating the Bayesian ID based on monitoring evidence, the obtained model was evaluated for several scenarios. In the base case, which assumes the occurrence of more fishing than recreation (assuming 3:1, respectively), the obtained utility is 18.64% (catches) and 31.96% (satisfaction). Of the scenarios run, the one that obtained the best results in the utility nodes together was the second one. The use of these tailored tools and approaches seems to be of fundamental importance for the adequate management of coastal infrastructures, particularly with regard to the inference of fishing resources and their sustainable use. An adequate interpretation based on the use of these tools implies being able to safeguard the ecological balance and economic sustainability of the communities operating in these areas. [ABSTRACT FROM AUTHOR]

The US Army Corps of Engineers (USACE) traditionally uses two metrics to evaluate the maintenance of coastal navigation projects: tonnage at the associated port (representing relative importance) and the controlling depth in the channel (representing operating condition). These are incorporated into a risk-based decision framework directing funds where channel conditions have deteriorated and the disrupted tonnage potential is the highest. However, these metrics fail to capture shipper demand for the maintained depth service provided by the USACE through dredging. Using automatic identification system (AIS) data, the USACE is pioneering new metrics describing vessel demand for the channel depth, represented by vessel encroachment volume (VEV). VEV describes the volume of the hull intruding into a specified clearance margin above the bed and captures how much vessels use the deepest portions of USACE-dredged channels. This study compares the VEV among 13 ports over 4 years by combining AIS, tidal elevations, channel surveys, and sailing draft. The ports are ranked based on the services demanded by their user base to inform the decision framework driving dredge funding allocations. Integrating demand-for-depth metrics into the Harbor Maintenance Fee assessment and/or Trust Fund disbursements could alleviate the constitutionality concerns and several criticisms levied against Harbor Maintenance funding. [ABSTRACT FROM AUTHOR]

A vessel automatic identification system (AIS) provides a large amount of dynamic vessel information over a large coverage area and data volume. The AIS data are a typical type of big geo-data with high dimensionality, large noise, heterogeneous densities, and complex distributions. This poses a challenge for the clustering and analysis of vessel sailing routes. This study proposes an efficient vessel sailing route clustering and analysis method based on AIS data that uses traffic density images to transform the clustering problem of complex AIS trajectories into an image processing problem. First, a traffic density image is constructed based on the statistics of the preprocessed AIS data. Next, the main sea route regions of traffic density images are extracted based on local image features, geometric structures, and spatial features. Finally, the sailing trajectories are clustered using the extracted sailing patterns. Based on actual vessel AIS data, multimethod comparisons and performance analysis experiments are conducted to verify the feasibility and effectiveness of the proposed method. These experimental results reveal that the proposed method displays potential for the clustering task of challenging vessel sailing routes. [ABSTRACT FROM AUTHOR]

This article takes a close look at the landscape of global navigation satellite system (GNSS) spoofing. It is well known that automated identification system (AIS) spoofing can be used for electronic warfare to conceal military activities in sensitive sea areas; however, recent events suggest that there is a similar interest of spoofing AIS signals for commercial purposes. The shipping industry is currently experiencing an unprecedented period of deceptive practices by tanker operators seeking to evade sanctions. Last year's announcement of a price cap on Russian crude oil and a new ban on Western companies insuring Russian cargoes is setting the stage for an increase in illegal activity. Our research team identified and documented the AIS position falsification by tankers transporting Russian crude oil in closed ship-to-ship (STS) oil transfers. The identification of the falsified positions is based on the repeated instances of discrepancies between AIS location suggestions and satellite radar imagery indications. Using the data methods at our disposal, we reconstructed the true movements of certain tankers and encountered some surprising behavior. These false ship positions make it clear that we need effective tools and strategies to ensure the reliability and robustness of AISs. [ABSTRACT FROM AUTHOR]

The significance of international maritime chokepoints and the exploration of their safety and security are intricately linked to the expansion of the maritime economy, the maintenance of political and social stability, and the safeguarding of state interests. Limited efforts have been dedicated to comprehensively assessing the extent of chokepoints' influence or establishing a global ranking of their importance using dependable maritime data. In light of the growing significance of oceans and seas in the realms of economy and society, there is a pressing need to afford heightened attention to the importance of chokepoints. In this paper, 15 critical chokepoints from around the world are studied, and the method of Location Quotient is used to calculate the influence of their radiation range utilizing the Automatic Identification System (AIS); this study charts the worldwide spatial and temporal dimensions of maritime transport spanning from 2012 to 2022. The conclusion of this study reveals the following key findings: (1) Maritime shipping trajectories exhibit fluctuating growth over time, with traffic hotspots predominantly located in continental border zones, gradually decreasing from the equator toward the poles; (2) The regions with active maritime traffic do not exhibit a positive correlation with the hotspots; instead, there is a pattern of "strong in the north, weak in the south, strong in the east, weak in the west"; (3) The Strait of Gibraltar and the Strait of Malacca are identified as the globally most strategically valuable straits for maritime shipping; (4) There is significant variation in the influence range of strategic passages, and countries with mutual dependencies may have competitive relationships. [ABSTRACT FROM AUTHOR]

With the popularization of vessel satellite AIS (automatic identification system) equipment and the continuous improvement of the AIS data's coverage, continuity and effectiveness, AIS has become an important data source to study the navigation characteristics of vessel groups. This study established an identification model to extract the fishing state and intensity information of fishing vessels, based on the AIS data of purse seine fishing vessels, combined with the variables of vessel position, speed and course. Expert experience, spatial statistics and data mining analysis methods were applied to establish the model, and the Western and Central Pacific Ocean areas were studied. The results showed that the overall accuracy of identification of the fishing state using Support Vector Machine method is higher, and the method has a good modeling effect. The spatial distribution characteristics of the vessels' fishing intensity based on AIS data showed a significant cluster distribution pattern. The obtained high-intensity fishing area can be used as a prediction of purse seine fishing grounds in the Western and Central Pacific areas. Through the processing and research of AIS data, this study provided important scientific support for the identification of fishing state of purse seine fishing vessels. The spatial fishing intensity of fishing vessels based on AIS data can also be used for the analysis of fishery resources and fishing grounds, and further serve the sustainable development of marine fisheries. [ABSTRACT FROM AUTHOR]

Sea fog is a disastrous weather phenomenon, posing a risk to the safety of maritime transportation. Dense sea fogs reduce visibility at sea and have frequently caused ship collisions. This study used a geographically weighted regression (GWR) model to explore the spatial non-stationarity of near-miss collision risk, as detected by a vessel conflict ranking operator (VCRO) model from automatic identification system (AIS) data under the influence of sea fog in the Bohai Sea. Sea fog was identified by a machine learning method that was derived from Himawari-8 satellite data. The spatial distributions of near-miss collision risk, sea fog, and the parameters of GWR were mapped. The results showed that sea fog and near-miss collision risk have specific spatial distribution patterns in the Bohai Sea, in which near-miss collision risk in the fog season is significantly higher than that outside the fog season, especially in the northeast (the sea area near Yingkou Port and Bayuquan Port) and the southeast (the sea area near Yantai Port). GWR outputs further indicated a significant correlation between near-miss collision risk and sea fog in fog season, with higher R-squared (0.890 in fog season, 2018), than outside the fog season (0.723 in non-fog season, 2018). GWR results revealed spatial non-stationarity in the relationships between-near miss collision risk and sea fog and that the significance of these relationships varied locally. Dividing the specific navigation area made it possible to verify that sea fog has a positive impact on near-miss collision risk. [ABSTRACT FROM AUTHOR]

Ionospheres over sea areas have an inevitable impact on maritime–satellite communications; however, due to geographic constraints, ionospheric observation and analysis over sea areas are far from adequate. In our paper, slant total electron content (STEC) along small-satellite constellation–automatic identification system (AIS) signal rays is used for computerized ionospheric tomography (CIT) over sea areas, and small-satellite constellations can provide more effective signal rays than a single satellite. An adjustment factor δ is introduced to optimize the initial electron density for the multiplicative algebraic reconstruction technique (MART). The CIT results reconstructed by a traditional MART and our new method at 00:00 and 06:00, 15 March 2022, are compared, and our new method produces about a 15% and over 40% improvement in average deviation (AD) and root-mean-square error (RMSE). The results show that the bigger the difference between δ and 1, the better improvement will be in the 3D CIT process. The initial electron density is well selected during CIT when δ is approximate to 1, which is the case at 12:00, and the reconstructed 3D electron density, applying the initial ne and the adjusted initial ne, are both close to the true electron density. The small-satellite constellation–AIS signals are valuable resources for electron density reconstruction in sea areas.

GNSS is an indispensable source of positioning, navigation and timing for many sectors, including inland waterway transport. Unfortunately, GNSS is also vulnerable to interference, including intentional jamming and spoofing. This paper evaluates the vulnerability of one of the key inland waterway systems—the automatic identification system (AIS)—to GNSS jamming and spoofing. The vulnerability is explored via a series of tests conducted in both laboratory and live-sky environments. The results clearly show the negative impact of both types of interference on AIS. The impact included denial of service and reporting of false position. Additionally, the effects on subsequent systems like river information services or nearby vessels are also showcased. The results presented provide valuable insight into the vulnerability of inland waterway transport. The need for understanding the system limitations and vulnerability rises with the increase in the implementation of autonomous systems into the inland waterway sector, as well as other critical infrastructure sectors. [ABSTRACT FROM AUTHOR]

As technologies such as eco-friendly ships, electric propulsion vessels, and multi-fuel propulsion systems advance, the scope of IoT applications in maritime fields is expanding, resulting in increased complexity in control factors. The gradual progression towards Maritime Autonomous Surface Ships (MASS) is further driving the evolution of ship-based IoT applications. These advancements underscore the necessity for a platform capable of ensuring reliable connectivity between ships and onshore. The limitations of the existing single cloud architecture become evident in this context. In response to these emerging challenges, this paper presents a cloud-based data platform structure anchored in the architecture of a multi-region fog cloud. Concurrently, we propose a strategic approach aimed at enhancing collision avoidance performance. This is achieved through the seamless sharing of navigation plan data among ships facilitated by the proposed platform structure. In regions densely populated with ships, there looms a potential for packet loss as data traffic sharing intensifies through the platform. To address this concern, we devised a traffic model based on the AIS data generation cycle and proposed an algorithm for subscription decision. Subsequently, we conducted comparative analyses of packet loss probabilities between the single cloud structure and the multi-region fog cloud structure. This was achieved through experimental packet loss data collected via the AWS cloud. Simulation results underscored a notable difference: with 100 subscribed ships, the packet loss probability in regions assuming a single cloud was about 28 times higher compared to the same region within the multi-region fog cloud structure. These simulations affirm the stable and effective implementation of the proposed collision avoidance performance enhancement method within the multi-region fog cloud structure. Furthermore, feasibility was corroborated through the successful implementation of the proposed platform via the AWS Cloud. [ABSTRACT FROM AUTHOR]

The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only provides positional and morphological characteristics without detailed identity information, presenting limitations as a sole data source. To address this issue, this paper proposes a method for enhancing maritime target identification and positioning accuracy through the fusion of Automatic Identification System (AIS) data and satellite remote-sensing imagery. The AIS utilizes radio communication to acquire multidimensional feature information describing targets, serving as an auxiliary data source to complement the limitations of image data and achieve maritime target identification. Additionally, the positional information provided by the AIS can serve as maritime control points to correct positioning errors and enhance accuracy. By utilizing data from the Jilin-1 Spectral-01 satellite imagery with a resolution of 5 m and AIS data, the feasibility of the proposed method is validated through experiments. Following preprocessing, maritime target fusion is achieved using a point-set matching algorithm based on positional features and a fuzzy comprehensive decision method incorporating attribute features. Subsequently, the successful fusion of target points is utilized for positioning error correction. Experimental results demonstrate a significant improvement in maritime target positioning accuracy compared to raw data, with over a 70% reduction in root mean square error and positioning errors controlled within 4 pixels, providing relatively accurate target positions that essentially meet practical requirements.

Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services.

Efficient night-time vessel detection is of significant importance for maritime traffic management, fishery activity monitoring, and environmental protection. With the advancement in object-detection approaches, the method of night-time vessel detection has gradually shifted from traditional threshold segmentation to deep learning that balances efficiency and accuracy. However, the restricted spatial resolution of night-time light (NTL) remote sensing data (e.g., VIIRS/DNB images) results in fewer discernible features and insufficient training performance when detecting vessels that are considered small targets. To address this, we establish an Enhanced Dense Nested-Attention Network (DNA-net) to improve the detection of small vessel targets under low-light conditions. This approach effectively integrates the original VIIRS/DNB, spike median index (SMI), and spike height index (SHI) images to maintain deep-level features and enhance feature extraction. On this basis, we performed vessel detection based on the Enhanced DNA-net using VIIRS/DNB images of the Japan Sea, the South China Sea, and the Java Sea. It is noteworthy that the VIIRS Boat Detection (VBD) observations and the Automatic Identification System (AIS) data were cross-matched as the actual status of the vessels (VBD-AIS). The results show that the proposed Enhanced DNA-net achieves significant improvements in the evaluation metrics (e.g., IOU, Pd, Fa, and MPD) compared to the original DNA-net, achieving performance of 87.81%, 96.72%, 5.42%, and 0.36 Wpx, respectively. Meanwhile, we validated the detection performance of Enhanced DNA-net and strong VBD detection against VBD-AIS, showing that the Enhanced DNA-net achieves 1% better accuracy than strong VBD detection.

This study proposes the use of satellite images and a vessel’s automatic identification system (AIS) data to evaluate the congestion level at container ports for operational efficiency analysis, which was never attempted in previous studies. The congestion level in container yards is classified by developing a convolutional neural network (CNN) model and an annotation tool to reduce the workload of creating training data. The annotation tool calculates the number of vertically stacked containers and the reliability of each container cell in a detection area by focusing on the shadows generated by the containers. Subsequently, a high-accuracy CNN model is developed for end-to-end processing to predict congestion levels. Finally, as an example of dynamic efficiency analysis of container terminals using satellite images, the relationship of the estimated average number of vertically stacked containers in the yard with the elapsed time between the image capture time and vessel arrival or departure time obtained from the automatic identification system data is analyzed. This study contributes to representing a prototype for dynamically estimating the number of vertically stacked containers and congestion level of container terminals using satellite images without statistical information, as well as its relationship with the timing of vessel arrival acquired from AIS data.

In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations comprising the geological and motion parameters of the vessels and associate them with respective vessels. The spatial and temporal variations inherent in these sequential observations make the association task challenging for traditional multi-object tracking algorithms. Additionally, the presence of overlapping tracks and missing data can further complicate the trajectory tracking process. To address these challenges, in this study, we approach this tracking task as a multivariate time series problem and introduce a 1D CNN-LSTM architecture-based framework for track association. This special neural network architecture can capture the spatial patterns as well as the long-term temporal relations that exist among the sequential observations. During the training process, it learns and builds the trajectory for each of these underlying vessels. Once trained, the proposed framework takes the marine vessel's location and motion data collected through the automatic identification system (AIS) as input and returns the most likely vessel track as output in real-time. To evaluate the performance of our approach, we utilize an AIS dataset containing observations from 327 vessels traveling in a specific geographic region. We measure the performance of our proposed framework using standard performance metrics such as accuracy, precision, recall, and F1 score. When compared with other competitive neural network architectures, our approach demonstrates a superior tracking performance. [ABSTRACT FROM AUTHOR]

Monitoring vessel activity is an important part of managing marine protected areas (MPAs), but small-scale fishing and recreational vessels that do not participate in cooperative vessel traffic systems require additional monitoring strategies. Marine Monitor (M2) is a shore-based, multi-sensor platform that integrates commercially available hardware, primarily X-band marine radar and optical cameras, with custom software to autonomously track and report on vessel activity regardless of participation in other tracking systems. By utilizing established commercial hardware, the radar system is appropriate for supporting the management of coastal, small-scale MPAs. Data collected in the field are transferred to the cloud to provide a continuous record of activity and identify prohibited activities in real-time using behavior characteristics. To support the needs of MPA managers, both hardware and software improvements have been made over time, including ruggedizing equipment for the marine environment and powering systems in remote locations. Case studies are presented comparing data collection by both radar and the Automatic Identification System (AIS) in urban and remote locations. At the South La Jolla State Marine Reserve near San Diego, CA, USA, 93% of vessel activity (defined as the cumulative time vessels spent in the MPA) was identified exclusively by radar from November 2022 through January 2023. At the Caye Bokel Conservation Area, within the Turneffe Atoll Marine Reserve offshore of Belize, 98% was identified exclusively by radar from April through October 2022. Spatial and temporal patterns of radar-detected and AIS activity also differed at both sites. These case study site results together demonstrate the common and persistent presence of small-scale vessel activity near coastal MPAs that is not documented by cooperative systems. Therefore, an integrated radar system can be a useful tool for independent monitoring, supporting a comprehensive understanding of vessel activity in a variety of areas. [ABSTRACT FROM AUTHOR]

As Chi na's shipping industry continues to develop, ship emissions have become a significant source of pollutants. Consequently, it has become imperative to comprehend accurately the nature and attributes of ship pollutant emissions and understand their causation and effect as a crucial aspect of pollution control and legislation. This paper employs high-precision automatic identification system (AIS) dynamic and static data, along with pollutant emission parameters, to estimate the pollutant emissions from a ship's main engine, auxiliary engine, and boiler using a dynamic approach. Additionally, the study considers the sailing state and trajectory of the vessel and analyzes the characteristics of ship carbon emissions. Taking Tianjin Port as an example, this study conducts a multi-dimensional analysis of pollutant emissions to gain insight into the causation and effect of pollutants based on the collected big AIS data. The results show that the pollutant emissions in this region are mainly concentrated in the vicinity of Tianjin Port land port area, Dagusha Channel, and the Main Shipping Channel of Tianjin Xingang Fairway. Carbon emissions peak in September and are lower in June and December. Through accurate analysis of pollutant emission sources and emission characteristics in the region, this paper establishes the regular relationship between pollutant emissions and possible influencing factors and provides data support for China to formulate accurate pollutant emission reduction policies and regulate ship construction technology and carbon trading. [ABSTRACT FROM AUTHOR]

To achieve environmental sustainability on ships, stakeholders should make efforts to reduce emissions. Port authorities are crucial to attain this goal by introducing new policies. This study takes the Port of Long Beach as an example to assess port-wide ship emissions and explain the significance of shore power policy. Additionally, the study considers the impact of disruptions, such as the COVID pandemic, on ship emissions. The analysis compares data from three years before and after the pandemic to examine the relationship between ship waiting times, quantities, and emissions. The findings indicate that the majority of port-wide ship emissions are generated by berthing or anchoring vessels, from ship auxiliary engines and boilers. Furthermore, ship congestion due to reduced port productivity during the pandemic significantly increased emissions from berthing and anchoring vessels, with the emission proportion increasing from 68% to 86%. Adopting the shore power policy has effectively reduced ship emissions in port areas, and increasing the number of ships utilising shore power will be instrumental in tackling excessive ship emissions. [ABSTRACT FROM AUTHOR]

Recently, with the development of marine space around the world, friction between maritime traffic and various marine activities is intensifying. In particular, different recommendations are being made for the buffer zone between offshore wind farms and maritime traffic, as the criteria vary significantly. In order to respond to these changes in marine space, conducted this study on the change of maritime traffic patterns before and after the installation of floating LiDAR (Light Detection And Ranging) in marine facilities and apply it to future changes in maritime traffic patterns. The maritime traffic data was based on Automatic Identification System (AIS) data, and it was targeted at cargo ships and tankers with regular traffic patterns. A trajectory and spatial analysis were performed based on the AIS in the marine space. As a result of analyzing the globally maritime traffic patterns targeting the location of the installation complex of LiDARs, it can be confirmed that the existing maritime traffic patterns change into three maritime traffic patterns. Furthermore, the study employed the Hausdorff-distance algorithm for clustering analysis, categorizing vessels with similar trajectories. This approach facilitated a locally analysis of the buffer zone associated with individual LiDAR, considering the length of the vessels. As a result, it was analyzed that each vessel navigated at different buffer zone depending on the size of the vessel, and it is possible for safe navigation and forecast the future maritime traffic patterns.

Objectives Aiming at the unstable error of the extended Kalman filter (EKF) and the limited representation ability of a single motion model in a scenario involving complex changes of a ship's motion state, a ship trajectory tracking algorithm based on an interactive multi-model (IMM) square root cubature Kalman filter (SCKF) is proposed. MethodsThe SCKF is introduced to replace the EKF in performing the trajectory tracking of automatic identification system (AIS) data; the constant velocity model (CVM), current statistical model (CSM) ，constant turn rate model (CTM) and improved CTM are combined using an interactive multi-model framework, and three combined models are constructed to characterize the motion state of the AIS trajectory. Trajectory tracking experiments are carried out using the three combined models.ResultsThe results show that in Trajectory 6, the root mean square error (RMSE) of the position information of the SCKF is smaller than that of the EKF, and the accuracy is improved by 30.06% when Combined Model 1 is used to track the trajectory with varying heading, heading rate and velocity; and when using Combined Model 3, the SCKF has the smaller fluctuation range of RMSE compared to the position information using the EKF in Trajectory 6, and the error value is reduced by 60.80%. Combined Model 3 has the best performance, but its computation is large. In a complex trajectory experiment at constant velocity, the performance of Combined Model 2 is close to that of Combined Model 3. ConclusionsThe proposed method can improve the accuracy of AIS data and ensure the stability of AIS data error fluctuation, making it possible to improve ship motion tracking and monitoring.

Global concerns regarding air quality have over the past decade led to the introduction of regulations by the International Maritime Organisation curbing the emissions of sulphur and nitrogen oxides (SOx, NOx). These limits were implemented initially in so-called "emission control areas", defined where the density of shipping activity combines with large coastal population centres such as northwest Europe or eastern USA. However, any legislation requires a scientifically robust and rigorous monitoring program to ensure compliance and prove attribution to an individual vessel. We argue the case for adherence to the mantra "faster, better, cheaper", where widespread adoption of independent low-cost solutions of onboard, in-stack sensors, combined with existing, globally ubiquitous satellite-based "automatic identification system" (AIS) data telemetry, provides an excellent solution to the affordable compliance and attribution conundrum for shipping companies and enforcement agencies alike. We present data from three field-campaigns which have significantly advanced the concept of onboard real-time monitoring of atmospheric ship emissions. [ABSTRACT FROM AUTHOR]

Kim, G.; Jeong, M.-H.; Jeon, S.-B., and Khan, M.S., 2023. Time-series analysis of ship movements using community detection and functional data analysis across the east coast of the Republic of Korea. Journal of Coastal Research, 39(2), 360–365. Charlotte (North Carolina), ISSN 0749-0208. Because of the recent climate change caused by air pollution in ports, global interest in marine spatial planning (MSP) has increased to achieve cost-effective navigation, economic development goals, logistics, and energy transportation. Accordingly, research on ship traffic, the main cause of air pollution in ports, is attracting attention. This study aims to investigate the temporal characteristics of the port network on the east coast of the Republic of Korea using the automatic identification system data set. This study used the Louvain model, a community detection technique for complex networks, and functional data analysis (FDA). The results of the Louvain model showed that the port network of the Republic of Korea has a hub-and-spoke structure in which ports that are close to each other are regionally clustered around the main ports in the community. The FDA results showed seasonal changes in port arrival volume and detection of abnormal ports in Korea's east-coast ports. The results of this study are expected to provide a baseline for policy makers to establish an effective MSP for port management, cost-effective route development, and prediction of the number of operations. [ABSTRACT FROM AUTHOR]

Arctic marine mammals have had little exposure to vessel traffic and potential associated disturbance, but sea ice loss has increased accessibility of Arctic waters to vessels. Vessel disturbance could influence marine mammal population dynamics by altering behavioral activity budgets that affect energy balance, which in turn can affect birth and death rates. As an initial step in studying these linkages, we conducted the first comprehensive analysis to evaluate the effects of vessel exposure on Pacific walrus (Odobenus rosmarus divergens) behaviors. We obtained >120,000 h of location and behavior (foraging, in‐water not foraging, and hauled out) data from 218 satellite‐tagged walruses and linked them to vessel locations from the marine automatic identification system (AIS). This yielded 206 vessel‐exposed walrus telemetry hours for comparison to unexposed hours, which we used to assess if vessel exposure altered walrus behavior. We developed a filter to account for misclassification of vessel exposure of telemetered walruses. Then we tested for an effect of vessel exposure on walrus behaviors using a combination of exact and propensity score‐based matching to account for confounding covariates, and we conducted statistical power analyses. We did not detect an effect of vessel exposure on walrus behaviors even when statistical power was high (i.e., for foraging walruses), which may have been due to the sample size‐driven need to define vessel presence within a larger than desired distance (15‐km measured radius) around a walrus. Although this study did not determine at what distance vessel exposure affects walrus behaviors, it provided an upper bound on the distance at which the vessels encountered may disturb foraging walruses. When more situation‐specific information is lacking, this distance could be used as a conservative buffer to maintain between vessels and areas of high use by foraging walruses. Studies on behavioral consequences of closer proximities between walruses and vessels are needed, and our assessments of misclassification rates and statistical power can be used for future studies. We demonstrated that analytical approaches such as matching, which are rarely used in wildlife studies, are particularly useful for testing hypotheses with observational data. [ABSTRACT FROM AUTHOR]

Satellite-derived Sea Surface Temperature (SST) and sea-surface Chlorophyll a concentration (Chl-a), along with Automatic Identification System (AIS) data of fishing vessels, were used in the examination of the correlation between fishing operations and oceanographic factors within the northern Indian Ocean from March 2020 to February 2023. Frequency analysis and the empirical cumulative distribution function (ECDF) were used to calculate the optimum ranges of two oceanographic factors for fishing operations. The results revealed a substantial influence of the northeast and southwest monsoons significantly impacting fishing operations in the northern Indian Ocean, with extensive and active operations during the period from October to March and a notable reduction from April to September. Spatially, fishing vessels were mainly concentrated between 20° N and 6° S, extending from west of 90° E to the eastern coast of Africa. Observable seasonal variations in the distribution of fishing vessels were observed in the central and southeastern Arabian Sea, along with its adjacent high sea of the Indian Ocean. Concerning the marine environment, it was observed that during the northeast monsoon, the suitable SST contributed to high CPUEs in fishing operation areas. Fishing vessels were widely distributed in the areas with both mid-range and low-range Chl-a concentrations, with a small part distributed in high-concentration areas. Moreover, the monthly numbers of fishing vessels showed seasonal fluctuations between March 2020 and February 2023, displaying a periodic pattern with an overall increasing trend. The total number of fishing vessels decreased due to the impact of the COVID-19 pandemic in 2020, but this was followed by a gradual recovery in the subsequent two years. For fishing operations in the northern Indian Ocean, the optimum ranges for SST and Chl-a concentration were 27.96 to 29.47 °C and 0.03 to 1.81 mg/m3, respectively. The preliminary findings of this study revealed the spatial–temporal distribution characteristics of fishing vessels in the northern Indian Ocean and the suitable ranges of SST and Chl-a concentration for fishing operations. These results can serve as theoretical references for the production and resource management of off-shore fishing operations in the northern Indian Ocean.

This article takes a close look at the landscape of global navigation satellite system (GNSS) spoofing. It is well known that automated identification system (AIS) spoofing can be used for electronic warfare to conceal military activities in sensitive sea areas; however, recent events suggest that there is a similar interest of spoofing AIS signals for commercial purposes. The shipping industry is currently experiencing an unprecedented period of deceptive practices by tanker operators seeking to evade sanctions. Last year’s announcement of a price cap on Russian crude oil and a new ban on Western companies insuring Russian cargoes is setting the stage for an increase in illegal activity. Our research team identified and documented the AIS position falsification by tankers transporting Russian crude oil in closed ship-to-ship (STS) oil transfers. The identification of the falsified positions is based on the repeated instances of discrepancies between AIS location suggestions and satellite radar imagery indications. Using the data methods at our disposal, we reconstructed the true movements of certain tankers and encountered some surprising behavior. These false ship positions make it clear that we need effective tools and strategies to ensure the reliability and robustness of AISs.

A vessel automatic identification system (AIS) provides a large amount of dynamic vessel information over a large coverage area and data volume. The AIS data are a typical type of big geo-data with high dimensionality, large noise, heterogeneous densities, and complex distributions. This poses a challenge for the clustering and analysis of vessel sailing routes. This study proposes an efficient vessel sailing route clustering and analysis method based on AIS data that uses traffic density images to transform the clustering problem of complex AIS trajectories into an image processing problem. First, a traffic density image is constructed based on the statistics of the preprocessed AIS data. Next, the main sea route regions of traffic density images are extracted based on local image features, geometric structures, and spatial features. Finally, the sailing trajectories are clustered using the extracted sailing patterns. Based on actual vessel AIS data, multimethod comparisons and performance analysis experiments are conducted to verify the feasibility and effectiveness of the proposed method. These experimental results reveal that the proposed method displays potential for the clustering task of challenging vessel sailing routes.

Abstract Arctic marine mammals have had little exposure to vessel traffic and potential associated disturbance, but sea ice loss has increased accessibility of Arctic waters to vessels. Vessel disturbance could influence marine mammal population dynamics by altering behavioral activity budgets that affect energy balance, which in turn can affect birth and death rates. As an initial step in studying these linkages, we conducted the first comprehensive analysis to evaluate the effects of vessel exposure on Pacific walrus (Odobenus rosmarus divergens) behaviors. We obtained >120,000 h of location and behavior (foraging, in‐water not foraging, and hauled out) data from 218 satellite‐tagged walruses and linked them to vessel locations from the marine automatic identification system (AIS). This yielded 206 vessel‐exposed walrus telemetry hours for comparison to unexposed hours, which we used to assess if vessel exposure altered walrus behavior. We developed a filter to account for misclassification of vessel exposure of telemetered walruses. Then we tested for an effect of vessel exposure on walrus behaviors using a combination of exact and propensity score‐based matching to account for confounding covariates, and we conducted statistical power analyses. We did not detect an effect of vessel exposure on walrus behaviors even when statistical power was high (i.e., for foraging walruses), which may have been due to the sample size‐driven need to define vessel presence within a larger than desired distance (15‐km measured radius) around a walrus. Although this study did not determine at what distance vessel exposure affects walrus behaviors, it provided an upper bound on the distance at which the vessels encountered may disturb foraging walruses. When more situation‐specific information is lacking, this distance could be used as a conservative buffer to maintain between vessels and areas of high use by foraging walruses. Studies on behavioral consequences of closer proximities between walruses and vessels are needed, and our assessments of misclassification rates and statistical power can be used for future studies. We demonstrated that analytical approaches such as matching, which are rarely used in wildlife studies, are particularly useful for testing hypotheses with observational data.

A trajectory is a sequence of observations in time and space, for examples, the path formed by maritime vessels, orbital debris, or aircraft. It is important to track and reconstruct vessel trajectories using the Automated Identification System (AIS) data in real-world applications for maritime navigation safety. In this project, we use the National Science Foundation (NSF)'s Algorithms for Threat Detection program (ATD) 2019 Challenge AIS data to develop novel trajectory reconstruction method. Given a sequence of N unlabeled timestamped observations X = {x1,x2,…,xN}, the goal is to track trajectories by clustering the AIS points with predicted positions using the information from the true trajectories X. It is a natural way to connect the observed point xî with the closest point that is estimated by using the location, time, speed, and angle information from a set of the points under consideration xi ∀ i ∈ {1, 2, …, N}. The introduced method is an unsupervised clustering-based method that does not train a supervised model which may incur a significant computational cost, so it leads to a real-time, reliable, and accurate trajectory reconstruction method. Our experimental results show that the proposed method successfully clusters vessel trajectories.

The rapid development and adoption of automatic identification systems as surveillance tools have resulted in the widespread application of data analysis technology in maritime surveillance and route planning. Traditional, manual, experience-based route planning has been widely used owing to its simplicity. However, the method is heavily dependent on officer experience and is time-consuming. This study aims to extract shipping routes using unsupervised machine-learning algorithms. The proposed three-step approach: maneuvering point detection, waypoint discovery, and traffic network construction was used to construct a maritime traffic network from historical AIS data, which quantitatively reflects ship characteristics by ship length and ship type, and can be used for route planning. When the constructed maritime traffic network was compared to the macroscopic ship traffic flow, the Symmetrized Segment-Path Distance (S S P D) metric returned lower values, indicating that the constructed traffic network closely resembles the routes ships transit. The result indicates that the proposed approach is effective in extracting a route from the maritime traffic network. [ABSTRACT FROM AUTHOR]