Detecting Search and Rescue Missions from AIS Data

Abstract

The crossing of the Mediterranean by refugees has turned to be an extremely perilous activity. Human operators that handle Search and Rescue (SAR) missions need all the help they can muster in order to timely discover and assist in the coordination of the operations. In this work we present a tool that automatically detects SAR missions in the sea, by employing Automatic Identification System (AIS) data streams. The approach defines three steps to be taken: a) trajectory compression for affordable real time analysis in the presence of big data; b) detection of sub-operations to which a SAR mission is actually decomposed, and; c) synthesis of multiple vessels' inferred behavior to determine an ongoing SAR mission and its details. The evaluation results are promising showing that AIS data carry highly valuable information even in the absence of any other type of data that could make the problem easier (e.g. coast guard signals).Keywords-search and rescue missions, AIS data. density based clustering, navigation pattern detection, trajectory mining, complex event processing.

Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection

“…The problem of anomaly detection in the maritime domain [8] has been the focus of research for many years, although in the recent years it started attracting more attention. From the early works on anomaly detection from Holst et al [9] and the later works of Varlamis et al [10] and Chatzikokolakis et al [11] on the detection of search and rescue patterns, several representation models and algorithms have been developed to increase maritime situation awareness, identify potential illegal activities and detect anomalous patterns in the vessels' trajectories.…”

“…In this work, we showcased a few real world examples which our model managed to accurately detect. Identifying specific cases of anomalous behavior [10,11,23,24] will allow us to fine-tune, improve and exploit the proposed unsupervised technique as a basis for a supervised model for the detection of events of interest in the maritime sector. As a future work, we intend to exploit the proposed network abstraction in order to identify events of interest to the maritime authorities.…”

A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification

“…The identification and management of SAR operations' patterns is a well-known research topic that has attracted a lot of interest [13][14][15]. The task of automating the surveillance of vessel trajectories in a region to detect when SAR patterns occur can be valuable for identifying when vessels perform a SAR mission or they may behave suspiciously.…”

“…This work presumes a list of SAR patterns as described in the "U.S. Coast Guard addendum to the United States National Search and Rescue supplement NSS" [20] which is extended with the patterns detected in Reference [13]. The list comprises the following SAR patterns:…”

“…In our previous work [13], we proposed an unsupervised method for detecting vessels potentially engaged in SAR missions, which was mainly based on the fact that when the vessel is performing a search or a rescue maneuver it is expected to change its course several times within a spatial and temporal distance. In that work, a trajectory simplification algorithm allowed to limit the trajectory points only to those representing major changes in the vessel's course over ground and a density based clustering algorithm allowed to locate the segments of the trajectory that contain many turn points at a close distance (the two parameters of the DBScan density based clustering algorithm, MinPts and eps actually define how many points at least must appear in a small radius around any point in order for it to be considered as core cluster point).…”