Port type prediction based on Machine Learning and AIS data analysis

Thomas Charrot , Juliette Guégan , Aldo Napoli (1) , Cyril Ray (2)

1 CRC - Centre de recherche sur les Risques et les Crises
2 Institut de Recherche de l'École navale

The estimated number of main ports and stationary areas in the world account for almost 25,000 and most of themare not well known. Being able to provide the navigator with information such as the type of surrounding ports in his navigation area is therefore of interest. Automatic Identification System data transmitted by ships is a valuable source of information whose potential can be exploited to give further knowledge onthe maritime situation. It is also useful to extract knowledgeabout ports’ activities and types. This paper aims at analysing AIS data using machine learning methods, and more specifically supervised classification to establish a harbour map with the objective of identify port’s type especially for the less documented areas of the globe.

Mots clés: Machine learning and classification methods; AIS data analysis; machine learning; port type classification; Machine learning and classification methods

Domaines: Intelligence artificielle [cs.AI]; Machine Learning [stat.ML]

Thomas Charrot, Juliette Guégan, Aldo Napoli, Cyril Ray. Port type prediction based on Machine Learning and AIS data analysis. IEEE/MTS OCEANS 2021, Sep 2021, San Diego, United States. ⟨10.23919/OCEANS44145.2021.9705864⟩. ⟨hal-03726772⟩