## 1 Introduction  
Maritime transportation represents approximately 90% of global trade by volume, placing safety and security challenges as a high priority for nations across the globe. Maritime surveillance data are collected at different scales and are increasingly used to achieve higher levels of situational awareness. Automatic Identification System (AIS) technology provides a vast amount of near-real time information, calling for an ever increasing degree of automation in transforming data into meaningful information to support operational decision makers. As an example, the Centre for Maritime Research and Experimentation (CMRE) is currently receiving an average rate of 600 Million AIS messages per month from multiple sources, and the rate is increasing [1]. AIS is a self-reporting messaging system originally conceived for collision avoidance (AIS is mandatory for ships of 300 gross tonnage and upwards in international voyages, 500 and upwards for cargoes not in international waters and passenger vessels [2]. In addition, fishing vessels greater than 15 m sailing in water under the jurisdiction of the European Union Member States shall also be required to be fitted with AIS [3].) to broadcast information on their location (positional, identification and other information) at a variable refresh rate, which depends on their motion (vessels at anchor transmit their position every two minutes and increase the broadcast rate up to two seconds when maneuvering or sailing at high speed; every five minutes, vessels transmit other data (static and voyage related information) containing identifiers, such as International Maritime Organization (IMO) number, call sign, ship name and Maritime Mobile Service Identity (MMSI), used as a primary key to link the message to position information. Static information also includes size, type of vessel and cargo, whereas voyage related data, such as Estimated Time of Arrival (ETA) and destination, are manually set and not fully reliable [4].) Over the last several years, the AIS data received by ships and coastal stations have been transmitted to regional or national data centers. When multiple receivers are connected into networks, certain challenges arise with data intermittency, resolving data redundancy received by multiple receivers, correcting errors in timestamps assigned by varying receivers and identifying tracks of vessels that erroneously share the message identifier. This level of pre-processing is necessary to extract maritime motion patterns, especially at a global scale. Receiving AIS messages from space [5] is becoming increasingly commonplace. As opposed to terrestrial networks of AIS receivers, whose performance is characterized by high persistence, but limited coverage, satellite-based systems can pick up messages in the open sea, far away from the coastline. Space-based receivers tend to be mounted on Low Earth Orbit (LEO) satellites, so the AIS coverage is global at the expense of persistence, due to the orbiting platform revisit time. It is clear that when integrating such systems with data received by terrestrial receivers, there are additional issues to resolve with variable frequency update, coverage and persistence. In this work, a methodology is presented that aims to convert the large amount of AIS data into decision support elements, independently of the number of receivers, their performance, the platform of origin and the scale of the area of interest. The knowledge is extracted via an incremental learning approach, in order to dynamically adapt to evolving situations (e.g., maritime seasonal patterns, operational conditions or changing routing schemes). This allows maritime traffic to be characterized following a fully unsupervised learning strategy with no _a priori_ information needed (_i.e._, using only raw AIS data). The proposed _traffic route extraction_ methodology can be used to provide up-to-date high level contextual information (e.g., Level 2 processing in the Joint Directors of Laboratories (JDL) model [6]).