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Coastal Marine Debris Detection and Density Mapping With Very High Resolution Satellite Imagery
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Researchers used high-resolution satellite imagery combined with machine learning to detect and map coastal marine debris density in southern Japan, finding that satellite-based methods can estimate debris amounts and types on beaches with reasonable accuracy.
Marine debris is a serious problem for marine ecosystems and related coastal activities. We carry out a study using in-situ debris clean-up data (collected by a local Japanese company) together with high spatial resolution satellite images to determine how well the satellite images can be used to estimate the amount and type of debris deposited on the beaches of the island in southern Japan. We use machine learning techniques to analyze the satellite images and find that Shannon's entropy computed from World-View 2 and 3 imagery from Maxar Corp. yields a useful detection and mapping of the coastal debris when compared with the in-situ clean-up data. We also assign a debris concentration to each satellite image pixel to visualize the distribution of the debris. The algorithm linking the satellite images to the ground truth clean-up data can now be used in areas where no ground truth data are available.
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Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.