Water transportation has always occupied a large proportion of China’s transportation and has become the key to China’s economic development. Water transportation is called “green transportation” in the industry due to the advantages of low transportation cost, high safety factor, and large capacity. However, water transportation has caused a great impact on the ecological environment of the waters for a long time, and solving the problem of sewage pollution from ships has also become an unavoidable problem. To control pollution from ships, it is essential to analyze the characteristics of ship domestic sewage discharge. In this study, a ship domestic sewage discharge complex network (SDCN) is established based on the ship voyage data to analyze the discharge characteristics in the Yangtze River. According to the topological analysis, the SDCN is a small-world network with the power-law degree distribution and superliner betweenness-degree correlation as distinguishing characteristics. The top five MDs with the highest in-vertex strength are Chaotianmen MD (S = 165,129,561), Tongling MD (S = 66,426,616), Maanshan MD (S = 62,087,158), Baqu MD (S = 59,964,550), and Shashi MD (S = 55,569,399), which indicates that these MDs receive a large amount of sewage. And the volume of domestic sewage between Chaotianmen MD and Baqu MD is the largest. The results can help us understand the discharge characteristics of ship domestic sewage and how they can be targeted to develop control measures for the countermeasures of “zero discharge” for inland ships.

Accurate vessel trajectory prediction from Automatic Identification System (AIS) records is important for maritime traffic monitoring, route planning, and intelligent vessel traffic management. However, reliable prediction remains challenging for long forecasting horizons and turning maneuvers. To address this problem, this study proposes the Geometry-Consistent Trajectory Transformer (GCT-Former), a progressive and refinement-based framework for AIS-based vessel trajectory prediction. The proposed model integrates multi-scale historical trajectory encoding, progressive residual future-position generation, and global–local trajectory refinement to improve the stability and continuity of long-horizon trajectory prediction. The predicted trajectories are evaluated as geometric future-position estimates and can provide trajectory-level information for downstream maritime traffic monitoring and decision-support applications. Experiments are conducted on three real-world Danish maritime regions: Aarhus Bay, Great Belt, and Skagen. Compared with representative conventional and deep-learning trajectory prediction models, the proposed model shows its most consistent advantage in long-horizon prediction, particularly in terms of ADE and FDE. In the long-term setting, it achieves average displacement errors of 0.344, 0.546, and 0.218 km and final displacement errors of 0.774, 1.368, and 0.525 km on Aarhus Bay, Great Belt, and Skagen, respectively. The ablation analysis further shows that removing the multi-scale encoding module increases the long-term average displacement error by 7%, 4%, and 3%, while removing the progressive residual decoder leads to larger increases of 15%, 9%, and 8% on the three datasets. The turning-maneuver analysis also shows lower geometric prediction errors under mild-turning and sharp-turning scenarios. These results indicate that GCT-Former improves AIS-based vessel trajectory prediction, especially for long-horizon and maneuvering cases.

Underwater marine debris detection is important for marine environmental monitoring, robotic inspection, and debris removal. However, reliable detection remains challenging because underwater images often suffer from low illumination, color distortion, turbidity, cluttered backgrounds, and weak boundaries. These factors reduce feature reliability and hinder accurate localization, especially for small, occluded, or low-visibility debris. To address these challenges, this paper proposes UADet, an adaptive detector for marine debris detection in degraded underwater scenes. UADet integrates two complementary components: Underwater Degradation-aware Feature Modulation (UDFM) and Visibility-aware Boundary Distribution Refinement (VBDR). UDFM extracts lightweight image-level degradation cues and modulates multi-scale features to improve robustness under varying underwater conditions. VBDR incorporates object scale and an appearance-based proxy for local visual difficulty into boundary distribution learning and matching cost, providing adaptive localization supervision for small objects and objects with weak visual evidence. Experiments are conducted on TrashCan and J-Litter, and UADet is compared with representative real-time detectors, including YOLOv8s, YOLOv10s, YOLOv11s, and RT-DETR. The results show that UADet achieves the best performance on both datasets, with 72.74% mAP@0.5, 81.36% precision, and 69.36% recall on TrashCan, and 48.02% mAP@0.5, 70.13% precision, and 55.61% recall on J-Litter. Compared with the strongest baseline, UADet improves mAP@0.5 by 3.12 percentage points on TrashCan and 4.83 percentage points on J-Litter. Ablation and qualitative analyses demonstrate that UDFM and VBDR provide complementary improvements. These results indicate that modeling underwater degradation and boundary uncertainty improves the robustness and reliability of marine debris detection in challenging underwater environments.

Abnormal ship behavior detection is important for maritime traffic surveillance, navigation safety, and risk prevention. However, existing methods often depend on handcrafted features or a single reconstruction or prediction signal, which limits their ability to detect both sustained trajectory abnormalities and abrupt vessel movement changes. This paper proposes a Dual-Error Fusion LSTM–Transformer framework, referred to as DEFLT, for AIS-based abnormal ship behavior detection. A motion-aware vessel representation was first constructed by combining the geographical position, speed over ground, course over ground, and their temporal variations. An LSTM autoencoder reconstructs historical trajectory windows, while a Transformer prediction module estimates subsequent vessel states. The standardized reconstruction and prediction errors are fused into a unified anomaly score to capture complementary evidence from historical trajectory inconsistency and unexpected future motion. Experiments were conducted using real-world AIS data collected during September 2019 from four representative Danish waters. The study considers four abnormal behaviors: speed anomalies, course anomalies, loitering, and route deviations. Compared with KNN, LOF, Isolation Forest, Random Forest, the LSTM-AE, and the Transformer, DEFLT achieves F1-scores of 0.96, 0.97, 0.88, and 0.93 across the four study areas. For type-specific detection, the Macro-F1 values range from 0.61 to 0.86, while Macro-Recall remains between 0.88 and 0.96. Friedman and post hoc Wilcoxon signed-rank tests further demonstrate that DEFLT provides a significant and consistent improvement over all baseline methods. These results verify the effectiveness of dual-error fusion for detecting heterogeneous abnormal ship behaviors from AIS trajectories. In operational settings, DEFLT can serve as an alert-prioritization tool for vessel traffic services and port authorities by directing attention to atypical trajectories that require timely review, thereby supporting safer and more resource-efficient maritime traffic coordination.

Accurate short-horizon forecasting of ship CO₂ emissions is important for maritime carbon monitoring, operational assessment, and the transition toward sustainable maritime transportation. However, reliable forecasting remains challenging because ship emissions are affected by temporally varying operating states, vessel-specific characteristics, and nonlinear emission relationships. This study proposes a Carbon-oriented Spatio-Temporal Graph Transformer (C-STGT) for forecasting STEAM-estimated ship CO₂ emissions from Automatic Identification System (AIS) trajectories. The proposed model integrates temporal representation learning, single-vessel graph modeling, vessel-specific attributes, carbon-related attention bias, and emission-oriented decoding to capture vessel operating and emission dynamics. C-STGT is evaluated on five containerships operating along the Shanghai–Los Angeles corridor and compared with ten baseline models. Experimental results show that C-STGT achieves a root mean squared error (RMSE) of $0.89$, a mean absolute error (MAE) of $0.51$, a weighted absolute percentage error (WAPE) of $3.11\%$, and an $R^2$ of $0.96$, outperforming the compared models. Ablation, emission-formulation sensitivity, and statistical analyses further support the effectiveness and robustness of the proposed framework. By improving short-horizon vessel-level carbon-emission forecasting, the proposed approach can support more timely emission monitoring and operational assessment, providing quantitative information for maritime operations and maritime decarbonization. In summary, C-STGT provides an effective forecasting framework that contributes to the development of more sustainable maritime transportation.