处理海量船舶轨迹数据，自动分组并识别异常航行行为。
STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data
- 用自定义BERT模型编码不等长轨迹，结合层次聚类分组。
- 在含数十亿条消息的全国数据上实现稳定聚类与异常分离。
- 无需预设聚类数，适合大规模海事监控与安全分析。
我们提出一种可扩展的框架，用于对来自万亿字节级自动识别系统（AIS）档案的海上轨迹进行无监督聚类。通过基于BERT的自定义模型对变长轨迹进行编码，并采用CURE层次聚类方法，生成无需预设聚类数量且具有物理可解释性的轨迹群组。基于重建误差和聚类噪声分配的内在无监督异常检测方法，可识别异常航行模式。该框架在覆盖一年期、包含数十亿条消息的国家级AIS数据集上进行了验证，实现了稳定的轨迹聚类，并清晰区分了正常与异常船舶行为。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。