Search Results (47)

Air pollution in urban and coastal regions is increasingly shaped by interactions among port operations, freight logistics, urban mobility systems, spatial development patterns, and governance arrangements. Although emission reduction technologies and regulatory measures have advanced significantly, their implementation frequently remains distributed across sector-specific institutional and operational domains. This paper introduces the concept of Air-Aware Port–City–Logistics Systems, proposing an integrated governance framework that treats air quality as a system-level outcome of linked maritime, logistics, urban transport, spatial, technological, and adaptive processes. Drawing on a structured interdisciplinary synthesis of literature in transport planning, maritime economics, logistics, environmental governance, air-quality management, and resilience, the study identifies key gaps in cross-sectoral coordination and in the governance of interdependencies influencing air-quality outcomes. The framework maps relationships among emission sources, transport and logistics flows, spatial configurations, population exposure, institutional coordination, technological and data systems, and adaptive capacity, thereby identifying potential intervention points across the port–city–logistics system. Its distinctive contribution lies not merely in combining these dimensions, but in organising them through a governance-oriented architecture in which monitoring, operational decisions, institutional responses, and adaptive learning are treated as interconnected processes. The framework also incorporates resilience and risk perspectives, emphasising adaptive governance approaches capable of responding to disruptions, changing demand patterns, climatic pressures, and other external environmental influences. By advancing a systems-based perspective, the paper contributes to air quality management and policy development through the integration of port–city interfaces, freight distribution networks, mobility planning, exposure considerations, and institutional decision-making within a unified analytical framework. As a conceptual framework, AAPCLS provides a transferable basis for future empirical application, contextual adaptation, and policy-oriented analysis rather than a validated operational model. Full article

Background: Maritime container shipping carries over 80% of global trade, yet compliance verification creates a confidentiality–verifiability conflict: carriers treat telemetry as commercially sensitive, while regulators, insurers, and port authorities require verifiable proof that cargo remained within specification. The EU Ecodesign for Sustainable Products Regulation (ESPR) mandates Digital Product Passports (DPPs), but no standardised DPP architecture exists for the multi-stakeholder maritime domain. Methods: We present Ocean DPP, a blockchain-anchored platform combining GS1 EPCIS 2.0, oneM2M, IOTA, and Groth16 zero-knowledge proofs (ZKPs), letting stakeholders verify compliance predicates without revealing raw sensor values; Merkle-tree batching reduces anchoring costs. We evaluate it in 16 experiments on a single-host testbed using synthetic workloads and a local IOTA network. Results: The platform achieved 95th-percentile latency of 48 ms without ZKP and 500 ms with proof generation, throughput of 7 events/s per host, 304 ms mean proof generation and 9.8 ms verification, 100% EPCIS 2.0 compliance, and zero permanent message loss across four failure-injection scenarios; horizontal scaling reduced the median latency by 37%. Conclusions: To the best of our knowledge, Ocean DPP is the first implemented, quantitatively evaluated platform integrating EPCIS 2.0, oneM2M, IOTA, and Groth16 ZKPs for privacy-preserving maritime DPPs; broader multi-host and public-network validation remains for future work. Full article

Ship detection in remote sensing images is crucial for maritime surveillance, port management, and national security. However, existing detectors struggle with complex harbor backgrounds, large-scale variations, and fine-grained inter-class similarities among diverse ship categories. In this paper, we propose SPFDet, a CLIP text-prior-guided structure-enhanced detector that systematically addresses these challenges by integrating vision-language semantic priors with channel-selective feature enhancement. First, a Semantic Prior Component (SPC) employs a frozen CLIP text encoder to generate category-level semantic embeddings from textual descriptions of ship types, which are combined with visual scene priors and detail-aware priors to provide hierarchical domain-specific guidance. Second, a Structure-Enhanced Transformer Module (SETM), equipped with a Cross-level Channel Selection Module (CCSM) and Multi-Head Cross-Attention (MHCA), selectively enhances discriminative channel responses associated with hull contours, deck textures, and fine structural patterns across encoder layers. Third, a Fused Category-Dominated Feature (FCDF) generation mechanism integrates CLIP-based semantic priors with multi-scale visual features through category-guided attention, producing category-aware representations for precise classification and localization. We further introduce a Category-Semantic Consistency Loss to enforce alignment between predicted features and CLIP text priors. Extensive experiments on HRSC2016 and ShipRSImageNet benchmarks demonstrate that SPFDet achieves 97.2% and 72.8% mAP respectively, providing consistent improvements over strong detection baselines while maintaining competitive inference speed. Full article

Autonomous ship maneuvering in harbor environments is increasingly supported by advances in model predictive control, reinforcement learning, digital twins, multi-sensor fusion, berth allocation, and multi-agent coordination. However, these developments are often studied as separate technological domains, while real harbor autonomy requires coordinated operation across vessels, port infrastructure, regulatory systems, cybersecurity mechanisms, and human supervisory processes. This study presents an architecture-oriented critical review of autonomous ship and harbor maneuvering research published between 2015 and May 2026. The review synthesizes literature from control engineering, maritime artificial intelligence, sensor fusion, digital twins, port logistics, cyber-physical systems, regulation, cybersecurity, and human–AI supervision. The analysis introduces two conceptual contributions: a layered cyber-physical taxonomy and an integration maturity model. The taxonomy organizes autonomous harbor maneuvering into seven interdependent layers: physical dynamics, perception and sensor fusion, prediction and state estimation, control, decision and coordination, digital twin federation, and regulatory–supervisory governance. The maturity model distinguishes isolated vessel autonomy, assisted coordination, shared digital synchronization, agent-based coordination, and fully federated maritime cyber-physical autonomy. The reviewed evidence shows substantial progress in individual layers, especially control, perception, digital twins, and berth allocation. However, major gaps remain in cross-layer synchronization, semantic interoperability, regulation-aware decision-making, cybersecurity integration, and validated ship–shore federation. To address these gaps, this study proposes a Federated Maritime Cyber-Physical Architecture for autonomous harbor maneuvering. The architecture integrates vessel autonomy cores, port intelligence cores, semantic federation middleware, agent-based negotiation, regulatory verification, cybersecurity safeguards, and human supervisory interfaces. This review argues that future progress in autonomous harbor operations depends not only on stronger algorithms, but on interoperable, explainable, regulation-aware, and cyber-resilient ship–shore ecosystems. Full article

Vessel traffic service systems in remote or newly established maritime regions face significant operational limitations due to the scarcity of historical AIS data, which undermines the reliability of trajectory-based situational awareness and collision risk assessment. Existing deep learning models, predominantly validated on data-rich major shipping corridors, suffer severe performance degradation under cross-domain deployment, rendering them impractical for vessel traffic management in underserved waters. To bridge this operational gap, this study proposes a Boundary-Aware Distillation and LoRA-Based Transfer (BD-LT) framework that enables reliable trajectory prediction with as few as 132 target-domain trajectories. The framework integrates HDBSCAN-based geographic-semantic domain partitioning, a Time-Aware Transformer with Time2Vec encoding for irregular AIS sampling, hybrid knowledge distillation with error-boundary gating for selective cross-domain transfer, and LoRA-based parameter-efficient adaptation to mitigate overfitting. Validated on NOAA full-scale AIS measurements, the framework reduces the 60 min Final Displacement Error by 35.2% relative to the no-framework baseline, consistently outperforming state-of-the-art models across all prediction horizons, with statistical reliability confirmed via bootstrap resampling. These results demonstrate the practical feasibility of deploying data-driven trajectory prediction in maritime regions where conventional approaches have historically been inapplicable, with direct implications for collision avoidance decision support and port approach traffic management. Full article

The rapid development of synthetic aperture radar (SAR) imaging technology has significantly enhanced maritime monitoring capabilities; however, SAR ship detection remains constrained by the limited scale and representation capacity of existing rotated bounding box datasets. Most publicly available datasets rely on horizontal annotations, which introduce redundancy and localization ambiguity in densely distributed and nearshore scenarios. Although rotated bounding boxes provide more precise geometric representation, large-scale multi-source rotated SAR datasets are still insufficient to support robust model training. To address this limitation, we construct a large-scale multi-source rotated SAR ship dataset (LMRD) consisting of 13,024 high-resolution image chips with over 38,000 annotated ship instances, covering multiple satellite sources, polarization modes, and diverse maritime environments, including offshore, nearshore, complex coastal, and densely distributed port scenes, thereby enhancing scene diversity and annotation precision. Furthermore, independent of the dataset construction, we propose a multi-domain feature fusion (MDF) framework built upon Oriented RCNN, which integrates high-frequency information and visual saliency cues to improve feature representation under complex backgrounds. Experimental results on the LMRD demonstrate that, compared with the baseline Oriented RCNN, the proposed MDF framework achieves a 2.7% improvement in average precision. Additional analysis indicates that the dataset characteristics and the multi-domain fusion strategy contribute to performance enhancement at different stages of the detection pipeline, validating the effectiveness of the proposed dataset for rotated ship detection while demonstrating the complementary role of multi-domain feature enhancement. Full article

Geopolitical volatility and conflict are increasingly altering the operating conditions of maritime transport by affecting route feasibility, service reliability, port operations, regulatory compliance, and energy-related decisions. However, the relevant literature remains fragmented across smart port studies, shipping optimization research, cybersecurity analysis, and resilience-oriented discussions. This review addresses that fragmentation by examining smart port and shipping optimization as interdependent components of maritime resilience rather than as separate efficiency-oriented domains. Methodologically, the paper adopts a structured, semi-systematic review design combining bibliometric mapping and thematic synthesis to identify the evolution, thematic structure, and major research gaps of the field. The review shows that smart port research highlights the resilience value of real-time visibility, interoperable data exchange, dynamic terminal control, digital twins, and cyber-secure infrastructure, while shipping-optimization research emphasizes conflict-aware routing, schedule recovery, network redesign, capacity reallocation, and fuel-related decision support. At the same time, the literature provides only limited integration across the port–shipping interface, where resilience is actually produced through coordination between nodes, networks, and governance arrangements. Based on this synthesis, the paper argues that future research should move beyond isolated technical solutions and develop more integrated approaches that jointly address digitalization, operational adaptation, security, and decarbonization under geopolitical stress. The review contributes by clarifying the intellectual structure of this emerging field and by proposing a more system-oriented perspective on maritime resilience. Full article

The maritime industry is under increased challenges of balancing operational effectiveness and environmental responsibility. This study examines the application of intelligent agents as a technology that can align these two goals in the triple-bottom-line model that involves social responsibility, environmental footprint, and economic sustainability. An agent architecture taxonomy is outlined and adapted to the maritime industry, distinguishing between reactive, deliberative, hybrid, and multi-agent systems (MAS). The application of these architectures is analysed throughout the maritime domain. In the ship-centric environment, the analysis highlights the role of agents in autonomous navigation, energy-efficient meteorological routing, and predictive maintenance. The analysis in the port and supply-chain domain demonstrates a shift towards decentralized asset orchestration and logistic coordination rather than centralized control. The paper outlines certain barriers to widespread adoption, namely the reality gap of simulation-based training and the lack of transparency in deep-learning models (“black box” problem). The paper concludes by outlining a future research agenda proposing a use of explainable artificial intelligence (XAI), high-fidelity simulation-to-real transfer, and communication protocol standardization to continue the trend of developing strong autonomous capabilities in sustainable maritime logistics. Full article

International shipping contributes approximately 3% of global carbon dioxide emissions while serving as the circulatory system of global commerce. The International Maritime Organization’s 2023 GHG Strategy mandates net-zero emissions by or around 2050, with indicative targets requiring a 20–30% reduction by 2030 and a 70–80% reduction by 2040. From a coastal and ocean conservation perspective, these targets represent more than climate mitigation—they offer an opportunity to reduce the maritime sector’s broader ecological footprint, including underwater noise pollution, chemical contamination from antifouling coatings, and the transfer of invasive species through biofouling. This article examines the role of artificial intelligence in supporting maritime decarbonization across multiple domains: voyage optimization, wind-assisted propulsion management, vessel automation, port coordination, predictive maintenance, ship design optimization, and hull maintenance robotics. Critically, the analysis also addresses AI’s own environmental footprint—the substantial energy demands of data centers that power these technologies—and emphasizes the importance of transparent accounting of AI-related emissions. The article proposes research directions that advance both climate objectives and marine ecosystem protection. Full article

The sustainable management of marine resources requires accurate knowledge of fishing activity patterns and their interaction with coastal infrastructure. Intelligent Transportation Systems (ITS) are increasingly applied in the maritime domain, where data-driven approaches enhance safety, efficiency, and sustainability. In this context, Automatic Identification System (AIS) data provide valuable insights into vessel behavior and fisheries management. This study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify fishing grounds, and a density map-based approach to recognize port locations. By integrating AIS data with machine learning techniques, the study detects and analyzes fishing vessel activities, providing deeper insights into behaviors such as fishing ground visit times, durations, and transitions between fishing grounds and ports. A case study in the Aalesund area of Norway demonstrates that DBSCAN effectively reveals fishing activity patterns relevant to regulatory oversight and spatial planning, while density mapping accurately identifies fishing ports. The findings highlight the potential of AIS-based analytics and clustering methods within maritime ITS frameworks to enhance situational awareness, support compliance with fisheries regulations, and contribute to sustainable marine resource management. Using 2023 AIS data from the Aalesund region, 6 recurrent fishing grounds and 15 port locations are identified, and size-stratified visit frequency and residence-time distributions are quantified together with monthly seasonality in ground usage. Full article

AI technologies are increasingly applied to optimize operations in both port and urban logistics systems, yet integration across the full maritime city chain remains limited. The objective of this study is to assess, using a simulation-based experiment, the impact of an AI-orchestrated control policy on the performance of port–city logistics relative to a baseline scheduler. The study proposes an AI-orchestrated approach that connects autonomous ships, smart ports, central warehouses, and multimodal urban networks via a shared cloud control layer. This approach is designed to enable real-time, cross-domain coordination using federated sensing and adaptive control policies. To evaluate its impact, a simulation-based experiment was conducted comparing a traditional scheduler with an AI-orchestrated policy across 20 paired runs under identical conditions. The orchestrator dynamically coordinated container dispatching, vehicle assignment, and gate operations based on capacity-aware logic. Results show that the AI policy substantially reduced the total completion time, lowered truck idle time and estimated emissions, and improved system throughput and predictability without modifying physical resources. These findings support the expectation that integrated, data-driven decision-making can significantly enhance logistics performance and sustainability in port–city contexts. The study provides a replicable pathway from conceptual architecture to quantifiable evidence and lays the groundwork for future extensions involving learning controllers, richer environmental modeling, and real-world deployment in digitally connected logistics corridors. Full article

With the development and widespread application of information technology, cybersecurity has become a focal point in all industry sectors. The maritime sector is no exception, with both physical and cyber threats. This survey first highlights, from a system engineering and information technology perspective, the specific architectures of on-vessel and in-port systems, as well as the communication equipment connecting them. Subsequently, cyber attacks in maritime and port domains and their potential consequences are described from various angles. Examples of real cases of cyber attacks are also reported. An overview of current key techniques used in vulnerability analysis, attack detection, and security protection is proposed before discussing cybersecurity issues in the maritime and port sectors from the particular perspective of discrete event systems. Various systems used in maritime and port domains are modeled as automata or Petri nets. Some analysis, detection, and protection approaches are then proposed to illustrate the potential of discrete event systems in this domain. Full article

Efficient spectrum allocation and telecom traffic steering in densified heterogeneous maritime communication networks remains a critical challenge due to user mobility, dynamic interference, and congestion across terrestrial, aerial, and sea-based transmitters. This paper introduces the Moving Colorable Graph (MCG) framework, a dynamic graph-theoretical representation of interferences that extends conventional graph coloring to capture the spatiotemporal evolution of heterogeneous wireless links under varying channel and traffic conditions. The formulated spectrum allocation problem is inherently non-convex, as it involves discrete frequency assignments, mobility-induced dependencies, and interference coupling among multiple transmitters and users, thus requiring suboptimal yet computationally efficient solvers. The proposed approach models resource assignment as a time-dependent coloring problem, targeting to optimally support users’ diverse demands in dense port-area networks. Considering a realistic port-area network with coastal, sea and Unmanned Aerial Vehicle (UAV) radio coverage, we design and evaluate three MCG-based algorithm variants that dynamically update frequency assignments, highlighting their adaptability to fluctuating demands and heterogeneous coverage domains. Simulation results demonstrate that the selective reuse-enabled MCG scheme significantly decreases network outage and improves user demand satisfaction, compared with static, greedy and heuristic baselines. Overall, the MCG framework may act as a flexible scheme for mobility-aware and congestion-resilient resource management in densified and heterogeneous maritime networks. Full article

Modern ports are pivotal to global trade, facing increasing pressures from operational demands, resource optimization complexities, and urgent decarbonization needs. This study highlights the critical importance of digital model adoption within the maritime industry, particularly in the port sector, while integrating sustainability principles. Despite a growing body of research on digital models, industrial simulation, and green transition, a specific gap persists regarding the intersection of port management, hydrogen energy integration, and Digital Twin (DT) applications. Specifically, a bibliometric analysis provides an overview of the current research landscape through a study of the most used keywords, while the document analysis highlights three primary areas of advancement: optimization of hydrogen storage and integrated energy systems, hydrogen use in propulsion and auxiliary engines, and DT for management and validation in maritime operations. The main outcome of this research work is that while significant individual advancements have been made across critical domains such as optimizing hydrogen systems, enhancing engine performance, and developing robust DT applications for smart ports, a major challenge persists due to the limited simultaneous and integrated exploration of them. This gap notably limits the realization of their full combined benefits for green ports. By mapping current research and proposing interdisciplinary directions, this work contributes to the scientific debate on future port development, underscoring the need for integrated approaches that simultaneously address technological, environmental, and operational dimensions. Full article

Ports and maritime operations generate massive real-time data streams, particularly from Automatic Identification System (AIS) signals, which are challenging to query effectively using natural language. This study proposes a prototype AISStream-MCP, a memory-augmented real-time maritime question-answering (QA) system that integrates live AIS data streaming with a Model Context Protocol (MCP) toolchain to support port operations decision-making. The system combines a large language model (LLM) with four MCP-enabled modules: persistent dialogue memory, live AIS data query, knowledge graph lookup, and result evaluation. We hypothesize that augmenting an LLM with domain-specific tools significantly improves QA performance compared to systems without memory or live data access. To test this hypothesis, we developed two prototype systems (with and without MCP framework) and evaluated them on 30 queries across three task categories: ETA prediction, anomaly detection, and multi-turn route queries. Experimental results demonstrate that AISStream-MCP achieves 88% answer accuracy (vs. 75% baseline), 85% multi-turn coherence (vs. 60%), and 38.7% faster response times (4.6 s vs. 7.5 s), with user satisfaction scores of 4.6/5 (vs. 3.5/5). The improvements are statistically significant (p < 0.01), confirming that memory augmentation and real-time tool integration effectively enhance maritime QA capabilities. Specifically, AISStream-MCP improved ETA prediction accuracy from 80% to 90%, anomaly detection from 70% to 85%, and multi-turn query accuracy from 65% to 88%. This approach shows significant potential for improving maritime situational awareness and operational efficiency. Full article