# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 1 Introduction  
The compulsory use of Automatic Identification System (AIS) for many vessel types, which has been enforced by naval regulations, has opened new opportunities for maritime surveillance (according to the Regulation 19 of SOLAS Chapter V ([[http://solasv.mcga.gov.uk/regulations/regulation19.htm](http://solasv.mcga.gov.uk/regulations/regulation19.htm)]([http://solasv.mcga.gov.uk/regulations/regulation19.htm](http://solasv.mcga.gov.uk/regulations/regulation19.htm))), since 2002 all vessels over 300 Gross Tonnage and all passenger vessels must carry an AIS transmitter). As a result, vessels become rich sources of AIS information that everyone can collect using an RF receiver. The collection, processing and visualisation of AIS data from multiple vessels has been the main business for many companies, such as MarineTraffic ([[https://www.marinetraffic.com/](https://www.marinetraffic.com/)]([https://www.marinetraffic.com/](https://www.marinetraffic.com/))), VesselTracker ([[https://www.vesseltracker.com/](https://www.vesseltracker.com/)]([https://www.vesseltracker.com/](https://www.vesseltracker.com/))), MariWeb ([[https://imisglobal.com/mariweb/](https://imisglobal.com/mariweb/)]([https://imisglobal.com/mariweb/](https://imisglobal.com/mariweb/))) and others that have develop and maintain their own world-wide networks of shore-based stations and regional data aggregators. Such platforms provide real-time information on ship's position and information on arrivals and departures from ports, using a network of shore stations that collect and pre-process AIS signals (for missing values, errors and redundancies) and forward the result to a centralized processing server for further cleaning, post-processing and visualization. Motivated by the abundance of AIS data and the rich information that they contain, a lot of data mining applications have been developed by researchers on top of historic AIS data, ranging from the automatic detection of events [1; 2] to the prediction of future vessel position [3] and estimation of the time of arrival at the port [4]. In a parallel line of research, several works on trajectory data analyticshave contributed platforms for the visualisation of vessel trajectories [5; 6], efficient handling of large volumes of such spatio-temporal data streams [7; 8] and methodologies for the abstraction of AIS data collected for a period and a region [9]. Maritime surveillance has been a topic that has attracted a lot of research and commercial interest over the last few years. The primary reason for this is that surveillance systems have significantly contributed to increasing the efficiency and safety of operations at sea. However, the analysis of AIS data collected for a region is a hard task to be performed manually, especially when it has to be performed for thousands of vessels that periodically transmit their position with high rates. It is therefore of utmost importance to support the maritime authorities with tools and methods that automatically process AIS data and provide automatic alerts that require human intervention [10; 11]. A critical requirement for such tools is that they exhibit the ability to foresee unfolding cautious and potentially hazardous situations, so as to propose measures of danger avoidance. _Search and Rescue_ (SAR) missions constitute a typical example that requires an automatic awareness system, in order to be detected directly from AIS data. _Search and Rescue_ (SAR) missions are taking place in the context of the refugee crisis in the Central and Eastern Mediterranean since 2015 [12]. The unnecessary loss of so many human lives indicates that SAR operations can benefit from situational awareness information either with the intention to identify them on time and to assist in the coordination of rescue ships but also in order to clarify the role of the involved vessels. At the macroscopic level, this knowledge can be even used to assess the impact of related policies.