Yu Yan

Intelligent recognition of ship motion states is a key technology for achieving smart maritime supervision and optimized port scheduling. To enhance both the modeling efficiency and recognition accuracy of AIS trajectory data, this paper proposes a ship behavior recognition method that integrates trajectory-to-image conversion with a convolutional neural network (CNN) for classifying three typical motion states: mooring, anchoring, and sailing. Firstly, a multi-step preprocessing pipeline is established, incorporating trajectory cleaning, interpolation complementation, and segmentation to ensure data completeness and consistency; secondly, dynamic features—including speed, heading, and temporal progression—are encoded into an RGB three-channel image, which not only preserves the original spatial and temporal information of the trajectory but also strengthens the dimension of the feature expression of the image. Thirdly, the lightweight CNN architecture (CNN-Lite) is designed to automatically extract spatial motion patterns from these images, with data augmentation techniques further enhancing model robustness and generalization across diverse scenarios. Finally, comprehensive comparative experiments are conducted to evaluate the proposed method. On a real-world AIS dataset, the proposed method achieves an accuracy of 91.54%, precision of 91.51%, recall of 91.54%, and F1-score of 91.52%—demonstrating superior or highly competitive performance compared with SVM, KNN, MLSTM, ResNet-50 and Swin-Transformer in both classification accuracy and model stability. These results confirm that constructing dynamic-feature-enriched RGB trajectory images and designing a lightweight CNN can effectively improve ship behavior recognition performance and provide a practical and efficient technical solution for abnormal anchoring detection, maritime traffic monitoring, and development of intelligent shipping systems.

In geographic information data processing, the matching of road data at different scales is crucial. Due to scale differences, road features can change, posing a challenge to multi-scale matching. Spatial relationship is the key to matching because it remains stable at different scales. In this paper, we propose an improved summation product of direction and distance (ISOD) descriptor, which combines features such as included angle chain and camber variance with similarity features such as length, direction, and Hausdorff distance to construct an integrated similarity metric model for multi-scale road matching. The experiments proved that the model achieved 94.75% and 93.34% precision and recall in 1:50,000 and 1:10,000 scale road data matching and 86.39% and 94.06% in 1:250,000 and 1:50,000 scale road data matching, respectively. This proves the effectiveness and practicality of the method. The ISOD descriptor and integrated similarity metric model in this paper provide an effective method for multi-scale road data matching, which helps the integration and fusion of geographic information data, and has an important application value in the fields of intelligent transport and urban planning.