Detecting vessels carrying migrants using machine learning

Political instability, conflicts and inequalities result into significant flows of people worldwide moving to different countries in search of a better life, safety or to ne reunited with their families. Irregular crossings into Europe via sea routes, despite not being new, have recently increased together with the loss of lives of people in the attempt to reach EU shores, highlighting the need to find ways to improve the understanding of what is happening at sea. This paper, intends to expand the knowledge available on practices among smugglers and contribute to early warning and maritime situational awareness. By identifying smuggling techniques and based on anomaly detection methods, behaviours of interest are modelled and one class support vector machines are used to classify unlabelled data and detect potential smuggling vessels. Nine vessels are identified as potentially carrying irregular migrants and refugees. Though further inspection of the results highlights possible misclassifications caused by data gaps and limited knowledge on smuggling tactics. Accepted classifications are considered subject to further investigation by the authorities.

SFYRIDIS Alexandros;  CHENG Tao;  VESPE Michele; 

2017-11-15

Harvard University

JRC106832