Seagull: Data-Driven Maritime Traffic Analysis

Abstract

Monitoring sea area use is essential for maritime safety, harbor management, and navigation planning. While the use of Automatic Identification System data has been studied extensively for trajectory-based tasks such as prediction, imputation, and collision prevention, such studies focus on individual vessel movements and do not provide area-level overviews of sea use—a key requirement for stakeholders such as harbor authorities and dredging agencies. We present the Seagull system for data-driven multi-level maritime traffic analysis. This system enables analyses of 16.4B AIS records from 101K vessels via a unified grid framework spanning from $40 \times 40 \text{km}$ cells, enabling regional traffic analyses, down to $2.1 \times 2.1 \mathrm{m}$ cells, enabling harbor-level analyses. By integrating with bathymetric depth models, the system supports safety-critical analyses involving under keel clearance, enabling identification of low-clearance zones and dredging needs. Employing a DuckDB star schema data warehouse for data storage, the system enables interactive analyses without pre-aggregation. Seagull is available online 11https://seagull.app.cs.aau.dk/.