Improving marine litter segmentation with limited resolution satellite imagery

This work proposes a learning-based semantic segmentation approach to detect floating plastic litter on the marine surface using Sentinel-2 satellite data. We adopt a convolutional network with a reduced number of parameters and, apart from the multispectral data, we feed specific indexes to assist plastic segmentation. Our approach compares favorably against other learning-based methods tailored for the same task.