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Maritime engineers have trained an energy shipping app to save over a quarter of a million tonnes of CO2 emissions by applying machine learning to its predictive system.

Maritime cargo capacity serves as a critical indicator of port efficiency and regional economic impact, yet reliable data remain constrained by operational and commercial complexities. This study addresses this gap by leveraging maritime big data to compare traditional empirical methods with machine learning approaches, integrating multi-source datasets (ship inbound/outbound records, vessel archives, and AIS data). Results demonstrate that the K-nearest neighbors (KNN) algorithm achieves 88% predictive accuracy on validation data-a 19-percentage-point improvement over conventional methods (69%). While training accuracy reached 95%, anomalous vessel operations in validation samples reduced performance to 88%, revealing the model's sensitivity to real-world variability and underscoring the need for enhanced data preprocessing. These findings highlight machine learning's potential to refine cargo capacity estimation while emphasizing the importance of robust data quality frameworks for operational deployment.

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Ground-breaking marine perception sensor provides high-definition situational awareness to eliminate at-sea collisions and allisions, and increase operational performance​​​​.

Sea fog is a severe marine environmental disaster that significantly threatens the safety of maritime transportation. It is a major environmental factor contributing to ship collisions. The Himawari-8 satellite's remote sensing capabilities effectively bridge the spatial and temporal gaps in data from traditional meteorological stations for sea fog detection. Therefore, the study of the influence of sea fog on ship collisions becomes feasible and is highly significant. To investigate the spatial and temporal effects of sea fog on vessel near-miss collisions, this paper proposes a general-purpose framework for analyzing the spatial and temporal correlations between satellite-derived large-scale sea fog using a machine learning model and the near-miss collisions detected by the automatic identification system through the Vessel Conflict Ranking Operator. First, sea fog-sensitive bands from the Himawari-8 satellite, combined with the Normalized Difference Snow Index (NDSI), are chosen as features, and an SVM model is employed for sea fog detection. Second, the geographically weighted regression model investigates spatial variations in the correlation between sea fog and near-miss collisions. Third, we perform the analysis for monthly time series data to investigate the within-year seasonal dynamics and fluctuations. The proposed framework is implemented in a case study using the Bohai Sea as an example. It shows that in large harbor areas with high ship density (such as Tangshan Port and Tianjin Port), sea fog contributes significantly to near-miss collisions, with local regression coefficients greater than 0.4. While its impact is less severe in the central Bohai Sea due to the open waters. Temporally, the contribution of sea fog to near-miss collisions is more pronounced in fall and winter, while it is lowest in summer. This study sheds light on how the spatial and temporal patterns of sea fog, derived from satellite remote sensing data, contribute to the risk of nea

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