Random Forest Classifier-based Ship Type Prediction with Limited Ship Information of AIS and V-Pass

Abstract

Identifying ship types is an important process to prevent illegal activities on territorial waters and assess marine traffic of Vessel Traffic Services Officer (VTSO). However, the TerrestrialAutomatic Identification System (T-AIS) collected at the ground station has over 50% of vessels that do not contain the ship type information. Therefore, this study proposes a method of identifying ship types through the Random Forest Classifier (RFC) from dynamic and static data of AIS and V-Pass for one year and the Ulsan waters. With the hypothesis that six features, the speed, course, length, breadth, time, and location, enable to estimate of the ship type, four classification models were generated depending on length or breadth information since 81.9% ofships fully contain the two information. The accuracy were average 96.4% and 77.4% in the presence and absence of size information. The result shows that the proposed method is adaptable to identifying ship types.