UEIKAP: space-based ship wake detection integrating contextual meteo-marine knowledge of local sea state

Abstract

Monitoring uncooperative vessels without transponders is of strategical interest both for the civil and military world. Ship detection lacks the ability to discern full situational knowledge of the vessel. However, moving ships generate wakes containing significant information -from current and possibly past position, heading, and speed, to vessel size and hull class. UEIKAP (Unveil and Explore the In-depth Knowledge of earth observation data for maritime Applications) is a project founded by the Italian Ministry of University and Research, and its objective is to develop a deep learning-based solution for wake detection in optical and synthetic aperture radar (SAR) spaceborne remote imagery. A dataset of real and simulated imagery is under development and will be used to train a landmark-based detection model able to exploit the characteristic features of ship wakes. This is accompanied by an in-depth sea characterization and meteo-marine conditions study, which is used to properly discriminate sea surface clutter for the objects of interest. All the results will be validated by test campaign at sea. This manuscript goes over the different types of data used to obtain the aforementioned contextual knowledge for project UEIKAP, from Automatic Identification System (AIS) data providers to sources of local meteo-marine information. Indications are provided regarding the integration of these inomogeneous data sources with the deep learning-based wake detection architecture. Information on the methods of the first data gathering campaign, held in July 2024 in Venice, is provided, accompanied by observations and preliminary results gathered from the experience.