Mining Vessel Trajectory Data for Patterns of Search and Rescue
Konstantinos Chatzikokolakis, Dimitrios Zissis, Giannis Spiliopoulos and Konstantinos Tserpes
BMDA, 2018
Abstract
The overall aim of this work is to explore the possibility of automatically detecting Search and Rescue (SAR) activity, even when a distress call has on yet been received. For this, we exploit a large volume of historical Automatic Identification System (AIS) data so as to detect SAR activity from vessel trajectories, in a scalable, data-driven supervised way, with no reliance on external sources of information (e.g. coast guard reports). Specifically, we present our approach which is based on a parallelised, nonparametric statistical method (Random Forests), which has proved capable of achieving prediction accuracy rates higher than 77%.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732310 and by Microsoft Research through a Microsoft Azure for Research Award.
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