Andriolo U., Gonçalves G., Hidaka M., Gonçalves D., Gonçalves L.M., Bessa F., Kako S. . Marine litter weight estimation from UAV imagery: Three potential methodologies to advance macrolitter r2024 · 記述言語： 日本語 出版者・発行元： Marine Pollution Bulletin In the context of marine litter monitoring, reporting the weight of beached litter can contribute to a better understanding of pollution sources and support clean-up activities. However, the litter scaling task requires considerable effort and specific equipment. This experimental study proposes and evaluates three methods to estimate beached litter weight from aerial images, employing different levels of litter categorization. The most promising approach (accuracy of 80 %) combined the outcomes of manual image screening with a generalized litter mean weight (14 g) derived from studies in the literature. Although the other two methods returned values of the same magnitude as the ground-truth, they were found less feasible for the aim. This study represents the first attempt to assess marine litter weight using remote sensing technology. Considering the exploratory nature of this study, further research is needed to enhance the reliability and robustness of the methods. DOI： 10.1016/j.marpolbul.2024.116405 Scopus PubMed
Kako S., Kataoka T., Matsuoka D., Takahashi Y., Hidaka M., Aliani S., Andriolo U., Dierssen H., van Emmerik T.H.M., Gonçalves G., Martinez-Vicente V., Mishra P., Monteiro J.G., Topouzelis K., Isobe A.2024 · 記述言語： 日本語 掲載種別： 研究論文（学術雑誌） 出版者・発行元： Marine Pollution Bulletin Effective reduction of oceanic plastic pollution requires scalable and objective monitoring methods that go beyond traditional human-based surveys. This review synthesizes recent advances in remote sensing and AI-driven image analysis for detecting macro-plastic litter. Peer-reviewed studies published up to 2024 were systematically selected from the Scopus database, focusing on applications of remote sensing platforms including webcams, drones, balloons, aircraft, and satellites for monitoring plastic litter in coastal, riverine, and other aquatic environments. Quantification methods ranged from manual annotation to deep learning-based models. Although machine learning has been increasingly adopted since around 2020, manual screening and rule-based approaches remain prevalent, reflecting the complexity of litter types and shapes. The review revealed considerable variability in quantification metrics—such as litter-covered area, volume, weight, and item count per unit area—which complicates cross-study comparisons and data harmonization. While remote sensing enhances spatial coverage, consistency, and repeatability, it faces persistent challenges, including environmental interference, limited resolution, and inconsistent protocols. Our findings highlight the urgent need for methodological standardization and harmonization of quantification units across platforms and geographic regions. Among available metrics, litter-covered area and item count per unit area are most suitable for cross-platform comparison. Continued development and integration of such technologies hold strong potential to facilitate science-based policymaking and long-term monitoring of plastic transport from land to ocean. DOI： 10.1016/j.marpolbul.2025.118630 Scopus PubMed
