Experimental Comparison of Ad Hoc Methods for Classification of Maritime Vessels Based on Real-Life AIS Data

Classification of Maritime Vessels is a recurrent task in maritime surveillance systems. Classification can be conducted by different methods, e.g., Naïve Bayes, k Nearest Neighbor, Decision Tree, Fuzzy Rule, or Neural Networks. The Automatic Identification System (AIS) is cooperative system of VHF-radio data exchange. By broadcasting of navigational data and ships' information it supports maritime safety, surveillance, and information. Based on measured AIS datasets of five maritime hotspots, easily implementable standard classification (i.e. ad hoc) methods from Data Science are compared to each other. These are evaluated in terms of accuracy. This experimental evaluation is motivated by the following question: Tip to which degree properties and behavior, e.g., vessel's type, can be detected by using large quantities of ship's positional, motion, and dimensions' data as provided by AIS? Future applications might include detection of fraudulent self-declarations of types, e.g., during illegal fishing activities.