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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 18056

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Special Issue Editors

Dr. Peng Peng

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Guest Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: geographical information science; spatial and temporal information modelling; complex network analysis; knowledge graph
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Feng Lu

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Guest Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: geographical information science; spatio-temporal databases; geo-spatial data mining; machine learning; complex network analysis; NLP; computational transportation science
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Prof. Dr. Yu Yang

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Guest Editor

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: energy geography; economic geography; international trade; complex network analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tianzhen Wang

E-Mail Website
Guest Editor

Department of Electrical Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: fault diagnosis; fault tolerance fault detection; control systems; control theory; tidal and wave power
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of data monitoring sensors, the maritime industry generates roughly 100-120 million data points every day, from different sources such as vessel movements. Through its application and insights, maritime big data are creating some new opportunities to drive innovation and deliver tangible operational efficiencies across the maritime domain. On the one hand, shipping companies are recognizing the utility of data to enrich and support decision-making processes that help predict, understand, and improve business operations and resilience. On the other hand, scholars or policy makers are conducting a real-time trade analysis on a fine spatial-temporal scale via maritime big data, with the aim of understanding global or regional macroeconomic trends and export trade trends. This Special Issue will highlight advances in the development, testing, and modeling of maritime information sensing and its applications. Submissions focusing on, but not limited to, the following areas are particularly welcome:

Large-scale maritime navigation multi-sensor tracking (AIS, remote sensing, radar, lidar, satellite, electromagnetic sensing, etc.);
Integration and monitoring, data fusion, AI and machine learning, analysis and visualization, and forecasting, planning and decision-making the maritime big data;
Maritime big data supporting international trade studies and decision making (maritime GIS, remote sensing, complex networks, etc.);
Real-time interfaces and new visualization and mapping methods for maritime big data;
Maritime big data applications (security, disaster prevention, protecting the environment, energy, etc.)

Dr. Peng Peng
Prof. Dr. Feng Lu
Prof. Dr. Yu Yang
Prof. Dr. Tianzhen Wang
Guest Editors

Manuscript Submission Information

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Keywords

maritime big data
international trade
maritime GIS
complex networks
visualization and mapping
data applications

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Published Papers (7 papers)

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Research

24 pages, 9500 KiB

Open AccessArticle

Trading Community Analysis of Countries’ Roll-On/Roll-Off Shipping Networks Using Fine-Grained Vessel Trajectory Data

by Shichen Huang, Tengda Sun, Jing Shi, Piqiang Gong, Xue Yang, Jun Zheng, Huanshuai Zhuang and Qi Ouyang

Sensors 2024, 24(22), 7226; https://doi.org/10.3390/s24227226 - 12 Nov 2024

Viewed by 372

Abstract

Roll-on/roll-off vessels (RO/RO vessels) are playing an increasingly critical role in international automobile transport, facilitating the efficient movement of vehicles and heavy machinery across continents. Despite this growing significance, there is still limited research specifically focused on the RO/RO shipping network and its impact on global trade. This paper studies the global RO/RO shipping network using AIS data on RO/RO vessels collected from 2020 to 2023. We construct a method based on the complex network theory and the graph feature extraction method to quantitatively assess the features of the RO/RO shipping network. This method assesses the complexity, sparsity, homogeneity, modularity, and hierarchy of the RO/RO shipping network across various ports and countries and employs the graph convolutional neural network (GCN) model to extract network features for community detection. This process enables the identification of port clusters that are frequently linked to RO/RO vessels, as well as regional transport modes. The paper’s findings support these conclusions: (1) From 2020 to 2023, the number of nodes in the RO/RO shipping network increased by 22%, primarily concentrated in African countries. The RO/RO shipping network underwent restructuring after the pandemic, with major complex network parameters showing an upward trend. (2) The RO/RO shipping network is complex, with a stable graph density of 0.106 from 2020 to 2023. The average degree increased by 7% to 4.224. Modularity decreased by 6.5% from 0.431 in 2022 to 0.403, while the hierarchy coefficient rose to 0.575, suggesting that post-pandemic, community routes have become more diverse, reflecting the reconstruction and maturation of the overall network. (3) The model yielded a silhouette coefficient of 0.548 and a Davies–Bouldin index of 0.559 using an improved automatic feature extraction method. In comparison between 2020 and 2023, the changes in the two indicators are small. This shows that GINs can effectively extract network features and give us results that we can understand for community detection. (4) In 2023, key communities divide the RO/RO shipping network, with one community handling 39% of global routes (primarily Europe–Asia), another community handling 23% (serving Asia–Pacific, Africa, and the Middle East), and a third community managing 38% (linking Asia, Europe, and South America). Full article

(This article belongs to the Special Issue Maritime Information Sensing and Big Data)

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23 pages, 2984 KiB

Open AccessArticle

BATMAN: A Brain-like Approach for Tracking Maritime Activity and Nuance

by Alexander Jones, Stephan Koehler, Michael Jerge, Mitchell Graves, Bayley King, Richard Dalrymple, Cody Freese and James Von Albade

Sensors 2023, 23(5), 2424; https://doi.org/10.3390/s23052424 - 22 Feb 2023

Cited by 1 | Viewed by 3105

Abstract

As commercial geospatial intelligence data becomes more widely available, algorithms using artificial intelligence need to be created to analyze it. Maritime traffic is annually increasing in volume, and with it the number of anomalous events that might be of interest to law enforcement agencies, governments, and militaries. This work proposes a data fusion pipeline that uses a mixture of artificial intelligence and traditional algorithms to identify ships at sea and classify their behavior. A fusion process of visual spectrum satellite imagery and automatic identification system (AIS) data was used to identify ships. Further, this fused data was further integrated with additional information about the ship’s environment to help classify each ship’s behavior to a meaningful degree. This type of contextual information included things such as exclusive economic zone boundaries, locations of pipelines and undersea cables, and the local weather. Behaviors such as illegal fishing, trans-shipment, and spoofing are identified by the framework using freely or cheaply accessible data from places such as Google Earth, the United States Coast Guard, etc. The pipeline is the first of its kind to go beyond the typical ship identification process to help aid analysts in identifying tangible behaviors and reducing the human workload. Full article

(This article belongs to the Special Issue Maritime Information Sensing and Big Data)

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18 pages, 7114 KiB

Open AccessArticle

A Quasi-Intelligent Maritime Route Extraction from AIS Data

by Shem Otoi Onyango, Solomon Amoah Owiredu, Kwang-Il Kim and Sang-Lok Yoo

Sensors 2022, 22(22), 8639; https://doi.org/10.3390/s22228639 - 9 Nov 2022

Cited by 7 | Viewed by 3296

Abstract

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. Full article

(This article belongs to the Special Issue Maritime Information Sensing and Big Data)

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24 pages, 6469 KiB

Open AccessArticle

How Does a Port Build Influence? Diffusion Patterns in Global Oil Transportation

by Peng Peng, Christophe Claramunt, Shifen Cheng and Feng Lu

Sensors 2022, 22(22), 8595; https://doi.org/10.3390/s22228595 - 8 Nov 2022

Cited by 1 | Viewed by 1507

Abstract

Ports play a critical role in the global oil trade market, and those with significant influence have an implicit advantage in global oil transportation. In order to offer a thorough understanding of port influences, the research presented in this paper analyzes the evolution of the dominance mechanisms underlying port influence diffusion. Our study introduces a port influence diffusion model to outline global oil transport patterns. It examines the direct and indirect influence of ports using worldwide vessel trajectory data from 2009 to 2016. Port influences are modelled via diffusion patterns and the resulting ports influenced. The results of the case study applied to specific ports show different patterns and influence evolutions. Four main port influence trends are identified. The first one is that ports that have a strong direct influence over their neighboring ports materialize a directly influenced area. Second, geographical distance still plays an important role in the whole port influence patterns. Third, it clearly appears that, the higher the number of directly influenced ports, the higher the probability of having an influence pattern, as revealed by the diffusion process. The peculiarity of this approach is that, in contrast to previous studies, global maritime trade is analyzed in terms of direct and indirect influences and according to oil trade flows. Full article

(This article belongs to the Special Issue Maritime Information Sensing and Big Data)

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21 pages, 8667 KiB

Open AccessArticle

AIS Trajectories Simplification Algorithm Considering Topographic Information

by Wonhee Lee and Sung-Won Cho

Sensors 2022, 22(18), 7036; https://doi.org/10.3390/s22187036 - 17 Sep 2022

Cited by 11 | Viewed by 2683

Abstract

With the development of maritime technology and equipment, most ships are equipped with an automatic identification system (AIS) to store navigation information. Over time, the size of the data increases, rendering its storage and processing difficult. Hence, it is necessary to transform the AIS data into trajectories, and then simplify the AIS trajectories to remove unnecessary information that is not related to route shape. Moreover, topographic information must be considered because otherwise, the simplified trajectory can intersect obstacles. In this study, we propose an AIS trajectory simplification algorithm considering topographic information. The proposed algorithm simplifies the trajectories without the intersection of the trajectory and obstacle using the improved Douglas–Peucker algorithm. Polygon map random (PMR) quadtree was used to consider topographic information on the coast, and the intersection between topographic information and simplified trajectories was efficiently computed using the PMR quadtree. To verify the effectiveness of the proposed algorithm, experiments were conducted on real-world trajectories in the Korean sea. The proposed algorithm yielded simplified trajectories with no intersections of the trajectory and obstacle. In addition, the computational efficiency of the proposed algorithm with the PMR quadtree was superior to that without the PMR quadtree. Full article

(This article belongs to the Special Issue Maritime Information Sensing and Big Data)

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15 pages, 3056 KiB

Open AccessArticle

Global Container Port Network Linkages and Topology in 2021

by Lu Kang, Wenzhou Wu, Hao Yu and Fenzhen Su

Sensors 2022, 22(15), 5889; https://doi.org/10.3390/s22155889 - 7 Aug 2022

Cited by 11 | Viewed by 3354

Abstract

The maritime transport of containers between ports accounts for the bulk of global trade by weight and value. Transport impedance among ports through transit times and port infrastructures can, however, impact accessibility, trade performance, and the attractiveness of ports. Assessments of the transit routes between ports based on performance and attractiveness criteria can provide a topological liner shipping network that quantifies the performance profile of ports. Here, we constructed a directed global liner shipping network (GLSN) of the top six liner shipping companies between the ports of Africa, Asia, North/South America, Europe, and Oceania. Network linkages and community groupings were quantified through a container port accessibility evaluation model, which quantified the performance of the port using betweenness centrality, the transport impedance among ports with the transit time, and the performance of ports using the Port Liner Shipping Connectivity Index. The in-degree and out-degree of the GLSN conformed to the power-law distribution, respectively, and their R-square fitting accuracy was greater than 0.96. The community partition illustrated an obvious consistence with the actual trading flow. The accessibility evaluation result showed that the ports in Asia and Europe had a higher accessibility than those of other regions. Most of the top 30 ports with the highest accessibility are Asian (17) and European (10) ports. Singapore, Port Klang, and Rotterdam have the highest accessibility. Our research may be helpful for further studies such as species invasion and the planning of ports. Full article

(This article belongs to the Special Issue Maritime Information Sensing and Big Data)

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21 pages, 4787 KiB

Open AccessArticle

A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns

by Yongfeng Suo, Yuxiang Ji, Zhenye Zhang, Jinhai Chen and Christophe Claramunt

Sensors 2022, 22(14), 5281; https://doi.org/10.3390/s22145281 - 14 Jul 2022

Cited by 8 | Viewed by 2215

Abstract

The successful emergence of real-time positioning systems in the maritime domain has favored the development of data infrastructures that provide valuable monitoring and decision-aided systems. However, there is still a need for the development of data mining approaches oriented to the detection of specific patterns such as unusual ship behaviors and collision risks. This research introduces a CSBP (complex ship behavioral pattern) mining model aiming at the detection of ship patterns. The modeling approach first integrates ship trajectories from automatic identification system (AIS) historical data, then categorizes different vessels’ navigation behaviors, and introduces a visual-oriented framework to characterize and highlight such patterns. The potential of the model is illustrated by a case study applied to the Jiangsu and Zhejiang waters in China. The results show that the CSBP mining model can highlight complex ships’ behavioral patterns over long periods, thus providing a valuable environment for supporting ship traffic management and preventing maritime accidents. Full article

(This article belongs to the Special Issue Maritime Information Sensing and Big Data)

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