TOWARDS DETECTING FLOATING OBJECTS ON A GLOBAL SCALE WITH LEARNED SPATIAL FEATURES USING SENTINEL 2

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1. Riverine litter monitoring from multispectral fine pixel satellite images S. Garaba & Y. Park 10.1016/j.envadv.2023.100451
2. Taking Artificial Intelligence Into Space Through Objective Selection of Hyperspectral Earth Observation Applications: To bring the “brain” close to the “eyes” of satellite missions A. Wijata et al. 10.1109/MGRS.2023.3269979
3. Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery A. Mohsen et al. 10.1007/s11356-023-27068-0
4. Deep learning for detecting macroplastic litter in water bodies: A review T. Jia et al. 10.1016/j.watres.2023.119632
5. Optical Detection of Marine Debris Using Deep Knockoff M. Olyaei et al. 10.1109/TGRS.2022.3228638
6. Detecting Marine pollutants and Sea Surface features with Deep learning in Sentinel-2 imagery K. Kikaki et al. 10.1016/j.isprsjprs.2024.02.017
7. On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring O. Karakuş 10.3389/frsen.2023.1302384
8. Meta-learning to address diverse Earth observation problems across resolutions M. Rußwurm et al. 10.1038/s43247-023-01146-0
9. Sentinel-2 Detection of Floating Marine Litter Targets with Partial Spectral Unmixing and Spectral Comparison with Other Floating Materials (Plastic Litter Project 2021) D. Papageorgiou et al. 10.3390/rs14235997
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