Anthropogenic litter density and composition data acquired flying commercial drones on sandy beaches along the Saudi Arabian Red Sea

Effectiveness of hierarchical Bayesian models for citizen science data with missing values: A case study on the factors influencing beach litter in Shimane Prefecture, Japan

Citizen science can play an important role in addressing the issue of marine debris. However, citizen science data are often composed of inconsistent methods compared to data collected by experts. In this study, we applied beach cleanup data, collected in different survey years at different survey sites, to a hierarchical Bayesian model to elucidate the factors affecting the distribution of beach litter. The results showed the model accounting for differences between years had a smaller Watanabe-Akaike Information criterion than the model that did not account for it, indicating better accuracy of the model. The amount of beach litter was influenced by current velocity and bay openness, and these effects varied across years. The results indicate that citizen science data, which may contain missing values due to various constraints such as economic and human resources, can make an important contribution toward solving marine debris issues by flexible statistical analysis methods.

Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review

Detection and identification of macroplastic debris in aquatic environments is crucial to understand and counter the growing emergence and current developments in distribution and deposition of macroplastics. In this context, close-range remote sensing approaches revealing spatial and spectral properties of macroplastics are very beneficial. To date, field surveys and visual census approaches are broadly acknowledged methods to acquire information, but since 2018 techniques based on remote sensing and artificial intelligence are advancing. Despite their proven efficiency, speed and wide applicability, there are still obstacles to overcome, especially when looking at the availability and accessibility of data. Thus, our review summarizes state-of-the-art research about the visual recognition and identification of different sorts of macroplastics. The focus is on both data acquisition techniques and evaluation methods, including Machine Learning and Deep Learning, but resulting products and published data will also be taken into account. Our aim is to provide a critical overview and outlook in a time where this research direction is thriving fast. This study shows that most Machine Learning and Deep Learning approaches are still in an infancy state regarding accuracy and detail when compared to visual monitoring, even though their results look very promising.