AI 中文总结
该研究提出STCAD框架，通过自定义BERT模型与CURE层次聚类实现TB级AIS轨迹的无监督聚类，结合重构损失与聚类噪声分配检测异常，在国家级AIS数据集上验证了方法的有效性。
AI 中文摘要
我们提出一种可扩展框架，用于对TB级自动识别系统（AIS）档案中的海事轨迹进行无监督聚类。变长轨迹通过自定义的基于BERT的模型编码，该模型经掩码 token 建模训练，再使用CURE层次聚类算法聚类，生成具有物理可解释性的轨迹组，无需预先定义聚类数量。一种基于重构损失和聚类噪声分配的内在无监督异常检测方法可识别不规则航行模式。该框架在包含数十亿条消息、跨度一年的国家级AIS数据集上进行验证，获得稳定的轨迹簇，并能清晰区分正常与异常船舶行为。
英文摘要
We present a scalable framework for unsupervised clustering of maritime trajectories derived from terabyte-scale Automatic Identification System (AIS) archives. Variable-length trajectories are encoded with a custom BERT-based model trained via masked token modeling and clustered using CURE hierarchical clustering, producing physically interpretable trajectory groups without requiring a predefined number of clusters. An intrinsic unsupervised anomaly detection method based on reconstruction loss and clustering noise assignment identifies irregular navigation patterns. The framework is demonstrated on a national-scale AIS dataset comprising billions of messages spanning one year, yielding stable trajectory clusters and a clear separation between nominal and anomalous vessel behavior.
CommentsAccepted at IGARSS 2026
