Vessel behaviour classification from AIS without geographical biases
Abstract
The automatic detection of vessel behaviours from Automatic Identification System (AIS) data is a challenging aspect of designing intelligent systems and aiding maritime situational awareness. The development of such systems remains limited to some activities like fishing, and by geographical biases that prevent systems to generalise to other areas than that used for training. To contribute to these questions, we investigate how to treat raw data or engineered features so that they do not convey such biases at training time and we propose methods for point-wise behaviour detection in the context of container vessels with four target behaviours. Several systems are studied, with raw data or engineered features as inputs, followed by shallow or deep learning classifiers. While good performances are obtained by several of them, we observe that a decision tree classifier with engineered features outperforms an LSTM in areas where no labelled data is available for training.
Domains
Machine Learning [cs.LG]
Origin : Files produced by the author(s)