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Abstract - This paper discusses machine learning and data mining approaches to analyzing maritime vessel traffic based on the Automated Information System (AIS). We review recent efforts to apply machine learning techniques to AIS data and put them in the context of the challenges posed by the need for both algorithmic performance generalization and interpretability of the results in real-world Maritime Situational Awareness (MSA) settings. We also present preliminary work on discovering and characterizing vessel stationary areas using an unsupervised spatial clustering algorithm. BibTex - @INPROCEEDINGS{CazzantiOCEANS2015, TITLE = {Mining Maritime Vessel Traffic: Promises, Challenges, Techniques}, AUTHOR = {L. Cazzanti and G. Pallotta}, BOOKTITLE = {{OCEANS}), PUBLISHER = {{IEEE/MTS}}, ADDRESS = {Genova, Italy}, MONTH = {May}, YEAR = {2015}} |