仅用AIS数据就能实时识别海峡中船舶类型，准确率超92%。
Machine Learning-Based Classification of Vessel Types in Straits Using AIS Tracks
- 基于轨迹特征构建机器学习模型，用AIS数据分类船舶类型。
- 随机森林模型准确率达92.15%，对货船与油船区分最易出错。
- 适合海事监管、打击非法捕捞的实战场景，轻量高效。
从自动识别系统（AIS）轨迹准确识别船舶类型对安全监管和打击非法、未报告及无管制（IUU）活动至关重要。本文提出一种基于机器学习的海峡尺度船舶分类方法，仅使用AIS数据。分析丹麦海事局提供的2025年1月22日至30日共八天的历史AIS数据，覆盖波罗的海博恩霍尔姆海峡。经航次记录前后填充、运动学与地理异常值剔除、按MMSI分段并排除静止时段（≥1小时）后，提取31个轨迹级特征，涵盖运动学（如船速统计）、时间、空间（哈弗辛距离、跨度）以及由AIS A/B/C/D参考点计算的船体形状属性（长度、宽度、长宽比、桥位比）。为防数据泄露，按MMSI进行分组训练/测试划分，并采用分层5折交叉验证。在五类船舶（货船、油轮、客船、高速艇、渔船；共1910条轨迹，测试集382条）上，树模型表现最优：使用SMOTE的随机森林达92.15%准确率（宏精确率94.11%，宏召回率92.51%，宏F1 93.27%），调优后的随机森林在一对多设置下达到最高ROC-AUC 0.9897。特征重要性分析表明桥位比和最大船速最具判别力；主要误判发生在货船与油轮之间，反映其航行行为相似。通过回填未知数据缺失的船型信息展示实际应用价值，并讨论使用DBSCAN进行行程分割和梯度提升集成模型以优化频繁停靠渡轮的性能提升方向。结果表明，仅用轻量级轨迹特征即可实现海峡环境下的实时船舶类型分类。
原文摘要 · Abstract (English)
Accurate recognition of vessel types from Automatic Identification System (AIS) tracks is essential for safety oversight and combating illegal, unreported, and unregulated (IUU) activity. This paper presents a strait-scale, machine-learning pipeline that classifies moving vessels using only AIS data. We analyze eight days of historical AIS from the Danish Maritime Authority covering the Bornholm Strait in the Baltic Sea (January 22-30, 2025). After forward/backward filling voyage records, removing kinematic and geospatial outliers, and segmenting per-MMSI tracks while excluding stationary periods ($\ge 1$ h), we derive 31 trajectory-level features spanning kinematics (e.g., SOG statistics), temporal, geospatial (Haversine distances, spans), and ship-shape attributes computed from AIS A/B/C/D reference points (length, width, aspect ratio, bridge-position ratio). To avoid leakage, we perform grouped train/test splits by MMSI and use stratified 5-fold cross-validation. Across five classes (cargo, tanker, passenger, high-speed craft, fishing; N=1{,}910 trajectories; test=382), tree-based models dominate: a Random Forest with SMOTE attains 92.15% accuracy (macro-precision 94.11%, macro-recall 92.51%, macro-F1 93.27%) on the held-out test set, while a tuned RF reaches one-vs-rest ROC-AUC up to 0.9897. Feature-importance analysis highlights the bridge-position ratio and maximum SOG as the most discriminative signals; principal errors occur between cargo and tanker, reflecting similar transit behavior. We demonstrate operational value by backfilling missing ship types on unseen data and discuss improvements such as DBSCAN based trip segmentation and gradient-boosted ensembles to handle frequent-stop ferries and further lift performance. The results show that lightweight features over AIS trajectories enable real-time vessel type classification in straits.
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