A Methodology to extract Geo-Referenced Standard Routes from AIS Data

TL;DR

The paper addresses the lack of a precise, ground-truth-free definition of standard maritime routes by introducing geo-referenced port-to-port trajectories derived from AIS data. It proposes an unsupervised, end-to-end pipeline that segments AIS tracks with a finite state machine, aggregates by departure and destination ports, and uses iterative density-based clustering (DBSCAN) to build standard routes, with semi-supervised learning to set clustering parameters. The approach is validated on a six-year AIS dataset spanning the Arctic and EMENA regions, achieving high route completeness (≈95.75%), strong destination accuracy (≈92%), and low outlier rates, demonstrating robust performance across vessel types. The work significantly enhances Maritime Situational Awareness by enabling longer-horizon trajectory reconstruction and pattern analysis for security applications, while outlining pathways for future improvements such as river routing fidelity and predictive capabilities up to $12$–$24$ hours.

Abstract

Maritime AIS (Automatic Identification Systems) data serve as a valuable resource for studying vessel behavior. This study proposes a methodology to analyze route between maritime points of interest and extract geo-referenced standard routes, as maritime patterns of life, from raw AIS data. The underlying assumption is that ships adhere to consistent patterns when travelling in certain maritime areas due to geographical, environmental, or economic factors. Deviations from these patterns may be attributed to weather conditions, seasonality, or illicit activities. This enables maritime surveillance authorities to analyze the navigational behavior between ports, providing insights on vessel route patterns, possibly categorized by vessel characteristics (type, flag, or size). Our methodological process begins by segmenting AIS data into distinct routes using a finite state machine (FSM), which describes routes as seg-ments connecting pairs of points of interest. The extracted segments are ag-gregated based on their departure and destination ports and then modelled using iterative density-based clustering to connect these ports. The cluster-ing parameters are assigned manually to sample and then extended to the en-tire dataset using linear regression. Overall, the approach proposed in this paper is unsupervised and does not require any ground truth to be trained. The approach has been tested on data on the on a six-year AIS dataset cover-ing the Arctic region and the Europe, Middle East, North Africa areas. The total size of our dataset is 1.15 Tbytes. The approach has proved effective in extracting standard routes, with less than 5% outliers, mostly due to routes with either their departure or their destination port not included in the test areas.

Figures (7)

• Figure 1: Methodology for naval route extraction: data processing pipeline.
• Figure 2: Visualization of extracted ports (example).
• Figure 3: Representation of offshore slots.
• Figure 4: Finite state machine used to extract segments from AIS messages.
• Figure 5: Three different segments representing a route from Norway, Sortland to Coastguard Patrol, Iceland.