Maritime ship traffic is globally increasing, with 90% of the world trade carried over the ocean. The emissions of marine traffic and coastal population, especially in ports and along shipping lanes with dense workloads, are a severe threat to the marine environment. Therefore, we propose a complete monitoring network to continuously monitor ship emissions by identifying oil soot, exhaust fumes and plastic litter on the sea surface. It is an intelligent integrated on-board system for spatial-spectral marine pollution analysis on buoys and static platforms. The system architecture consists of spectral vision systems (VIS, IR-thermal) with radiometers (UV-VIS-NIR) for spot data analysis. The study describes the proposed sensor system architecture evaluated with synthetic data analysis using a state-of-the-art Deep Learning algorithm. Combining our sensor system with other environmental observations will eventually integrate multi-sensor information towards a reliable holistic situational awareness of the marine ecosystem.
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