Empowering Sustainability Through AI-Driven Monitoring: The DEEP-PLAST Approach to Marine Plastic Detection and Trajectory Prediction for the Black Sea

Marine plastic pollution represents a critical ecological challenge, exerting long-lasting impacts on ecosystems, biodiversity, and human well-being. This study introduces the DEEP-PLAST project, an integrated AI-based framework designed for the detection and trajectory prediction of floating marine plastic waste using open-access Sentinel-2 satellite imagery and environmental models of ocean currents and wind. The DEEP-PLAST methodology integrates object detection (YOLOv5 on UAV data), semantic segmentation (U-Net/U-Net++ on Sentinel-2), and drift simulation using Copernicus and NOAA datasets. U-Net++ achieved the best performance (F1 = 0.84, false positive rate 5.2%), outperforming other models. Detected debris locations were linked to Lagrangian drift models to identify accumulation zones in the Black Sea, supporting targeted cleanup efforts. While promising, drift validation remains qualitative due to limited ground truth, to be addressed in future work with in situ and NGO data. This approach supports EU Mission Ocean, the Marine Strategy Framework Directive, and UN SDGs, demonstrating the potential of AI and remote sensing for marine protection. Future efforts will expand datasets, apply the platform to other seas, and launch a web tool for NGOs and policymakers.