Vessel behaviour classification from AIS without geographical biases - Aix-Marseille Université Access content directly
Conference Papers Year : 2022

Vessel behaviour classification from AIS without geographical biases

Raphael Sturgis
  • Function : Author
  • PersonId : 1164065
Valentin Emiya
Pierre Garreau
  • Function : Author
  • PersonId : 1164074


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.
Fichier principal
Vignette du fichier
ITSC_2022-1.pdf (1.57 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03775942 , version 1 (13-09-2022)


  • HAL Id : hal-03775942 , version 1


Raphael Sturgis, Valentin Emiya, Basile Couëtoux, Pierre Garreau. Vessel behaviour classification from AIS without geographical biases. IEEE International Conference on Intelligent Transportation Systems, Oct 2022, Macau, China. ⟨hal-03775942⟩
30 View
76 Download


Gmail Facebook X LinkedIn More