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Using artificial intelligence to support marine macrolitter research: A content analysis and an online database
Politikos, D.V.; Adamopoulou, A.; Petasis, G.; Galgani, F. (2023). Using artificial intelligence to support marine macrolitter research: A content analysis and an online database. Ocean Coast. Manag. 233: 106466. https://dx.doi.org/10.1016/j.ocecoaman.2022.106466
In: Ocean & Coastal Management. Elsevier Science: Barking. ISSN 0964-5691; e-ISSN 1873-524X, meer
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| Trefwoorden |
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| Author keywords |
Beached/dune; Seafloor; Deep learning; Machine learning |
| Auteurs | | Top |
- Politikos, D.V.
- Adamopoulou, A.
- Petasis, G.
- Galgani, F.
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| Abstract |
Marine scientists use a variety of collection and monitoring methods to survey macrolitter in aquatic environments, aiming to assess the level of pollution and design mitigation actions. However, the large volume of collected data often makes the visual recognition and identification of macrolitter items a time-consuming and labor-intensive task, indicating the need for automated and low-cost solutions. In addition, modelling approaches are needed to identify which environmental and anthropogenic factors shape the variability of observed litter concentrations. Artificial intelligence (AI) has emerged over the last years as a promising tool to address these issues. This study provides a literature review of published research that uses AI to process macrolitter datasets derived from imagery and tabular data. The focus is on diverse topics (litter domain, dataset source, sampling system, data type, task to be resolved, region, proposed methodologies, usability) with the aim of identifying the versatile contribution of AI on this theme and providing a reference resource for marine litter scientists. To do so, we release an online database (available here), in which the user can seek publications based on several categories and tags. Current limitations, challenges and potential future directions are also discussed. |
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