Priyadarshini, R.Arya, V.Sowmya Kamath, S.2026-02-0620242024 IEEE Space, Aerospace and Defence Conference, SPACE 2024, 2024, Vol., , p. 454-458https://doi.org/10.1109/SPACE63117.2024.10668301https://idr.nitk.ac.in/handle/123456789/28936Marine debris present a severe, escalating threat to oceans and coastal ecosystems, requiring effective monitoring and detection. This work proposes an automated marine debris detection system utilizing satellite imagery data from the MARIDA dataset, sourced from Sentinel-2. Advanced AI techniques are leveraged to analyze high-resolution satellite imagery, and the models are trained to facilitate the identification/tracking of marine debris across various water bodies. Experiments reveal that the machine learning models form a robust baseline, while the UNet model achieves improved precision. The proposed Attention-activated UNet model achieved the best performance, particularly in challenging conditions. © 2024 IEEE.artificial intelligenceMarine debris managementregion segmentationsatellite data processing