[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85052-en":3,"doc-seo-85052-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85052,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Geometry-Informed Maritime Anomaly Detection Using Probabilistic Roadmaps","Maritime anomaly detection underpins navigational safety and the protection of critical underwater infrastructure. The paper presents a geometry-informed supervised framework for identifying anomalous vessel trajectories in the Baltic Sea from AIS data. A Probabilistic Roadmap (PRM) is built on the navigable domain and used as a structural prior to project trajectories onto feasible corridors. Interpretable voyage-level features capture route efficiency, geometric deviation, kinematic variability, and cable proximity. Synthetic, infrastructure-aware anomalies address label scarcity for balanced training, and a Random Forest achieves test ROC AUC of 0.837.","Geometry-Informed Maritime Anomaly Detection Using Probabilistic  \nRoadmaps  \nGabriele Oliva∗ , Andrea Tomei, and Roberto Setola  \narXiv :2607 .08100v1 [ ee ss . SY] 9 Jul 2026  \nAbstract—Maritime anomaly detection is essential for navigational safety and for the protection of critical underwater infrastructure. This paper proposes a geometry-informed supervised framework for detecting anomalous vessel trajectories in the Baltic Sea using Automatic Identification System (AIS) data. A Probabilistic Roadmap (PRM) is constructed over the navigable maritime domain and used as a structural prior to project trajectories onto feasible corridors. This representation enables the extraction of interpretable voyage-level features capturing route efficiency, geometric deviation from nominal paths, kinematic variability, and proximity to submarine cables. To address the scarcity of labeled anomalous events, synthetic anomalies are generated through controlled trajectory perturbations and infrastructure-aware distortions, producing a balanced dataset for supervised training. A Random Forest classifier is trained on the resulting feature set and evaluated under cross-validation and a held-out test split. Experimental results show stable generalization performance, achieving a test ROC AUC of 0.837, indicating the effectiveness of embedding navigational feasibility constraints into the anomaly detection process. The proposed approach provides an interpretable and operationally relevant framework for infrastructure-aware maritime monitoring in geometrically complex environments.  \nIndex Terms—Maritime anomaly detection, AIS data, probabilistic roadmaps, Random Forest, underwater infrastructure security.  \nI. INTRODUCTION  \nMaritime anomaly detection is a fundamental component of maritime domain awareness, supporting navigational safety, regulatory compliance, and the protection of critical infrastructure. The increasing availability of data from the Automatic Identification System (AIS) has enabled a new generation of data-driven monitoring frameworks, where vessel trajectories are analyzed to identify deviations from expected behavior [1]–[4] . Recent developments increasingly rely on machine learning techniques capable of modeling complex spatiotemporal patterns in vessel motion [5], [6] .  \nEarly approaches focused on unsupervised modeling of nominal traffic patterns [7], [8] . Trajectory-based learning frameworks evolved toward clustering and probabilistic route modeling [9],[10], followed by deep generative architectures such as Variational Recurrent Neural Networks and GeoTrackNet [11], [12] . More recently, hybrid graph-based and  \nDepartment of Engineering, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy.  \n∗ Corresponding author. Email: [g.oliva@unicampus.it](g.oliva@unicampus.it)  \nThis work was partly supported by project VIGIMARE, funded by the European Union under grant n. 101168016. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This work was partly supported by Italian National project IMPROVE, funded by the Italian Ministry of Defense under grant n. 20711.  \nboosting-based approaches have been proposed for real-time or infrastructure-aware monitoring [13]–[15] .  \nDespite their strong predictive performance, most existing methods rely primarily on latent behavior modeling or learned feature embeddings, without explicitly encoding navigational feasibility constraints arising from geography, coastlines, and restricted areas. As a result, geometric consistency is often inferred implicitly rather than structurally embedded in the anomaly definition.  \nThe Baltic Sea represents a particularly challenging and strategically sensitive case study. It is characterized by dense traffic, archipelagos, narrow passages, and a high concentrat","cbCaimmAKSUw1SVL","https://ap.wps.com/l/cbCaimmAKSUw1SVL","pdf",755456,3,1,6,"English","en",105,"# Introduction\n# Proposed Framework","[{\"question\":\"How does the method use Probabilistic Roadmaps (PRMs) in maritime anomaly detection?\",\"answer\":\"It constructs a PRM over the navigable maritime domain and projects AIS trajectories onto this navigability graph, treating the PRM as a structural prior for feasible maritime corridors.\"},{\"question\":\"What kinds of features are extracted for classification?\",\"answer\":\"The framework extracts interpretable voyage-level features such as route efficiency, geometric deviation from nominal paths, kinematic variability, and proximity to submarine cables.\"},{\"question\":\"How are training labels for anomalous events addressed?\",\"answer\":\"Because labeled anomalies are scarce, the paper generates synthetic anomalies by applying controlled 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