ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 53-60, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-53-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
19 Oct 2017
DETECTING VESSELS CARRYING MIGRANTS USING MACHINE LEARNING
A. Sfyridis1, T. Cheng1, and M. Vespe2 1SpaceTime Lab, University College London, Gower Street, London WC1E 6BT, UK
2European Commission, Joint Research Centre (JRC), Directorate for Space, Security and Migration, Demography, Migration and Governance Unit, Via E. Fermi 2749, I-21027 Ispra (VA), Italy
Keywords: Machine Learning, SVM, Anomaly Detection, AIS, GPS, Data Mining, Pattern Recognition Abstract. 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 be 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. This highlights 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.
Conference paper (PDF, 2291 KB)


Citation: Sfyridis, A., Cheng, T., and Vespe, M.: DETECTING VESSELS CARRYING MIGRANTS USING MACHINE LEARNING, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 53-60, https://doi.org/10.5194/isprs-annals-IV-4-W2-53-2017, 2017.

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