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.
Evolution of river delta is highly related to the deposition and re-suspension of sediments. At the interacting zone of fresh river discharge and seawater, suspended sediments concentration (SSC) can vary sharply from a few mg/L to thousands of mg/L; thus, mapping the distribution of SSC will provide the first information about sediments transportation. The high spatial resolution (30 m) and high revisit frequency (2 day) of CCD imager on board the Chinese environment-monitoring satellite constellation: HJ-1A and HJ-1B, enable an effective observation of the fine dynamics of suspended sediments. In this work, three intensive cruises in the flooding season and dry season of Yellow River, were carried out to explore the SSC retrieval algorithms on the basis of HJ-1 CCD imageries. Quasi-simultaneous in-situ SSC data were collected with the pass of HJ-1 over the Yellow River Estuary and its vicinity waters, and a local empirical retrieval algorithm of SSC was established against the TOA (top of atmosphere) reflectance of HJ-1 CCD bands with the correction of Rayleigh scattering. This algorithm can be applied to very turbid waters with thousands of mg/L of SSC.
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