ADOPT

Marine litter is a growing ecologic concern that needs to be addressed at a global scale. While satellite and meteorological data is available in abundance, few advances have been made towards continuous monitoring of marine litter aggregated in floating patches on the oceans. To address this, we propose ADOPT, a hybrid model to automatically identify marine litter from satellite imagery and predict its location given meteorological information. In the first instance, ADOPT contains a deep learning-based detection module to identify aggregations of floating debris. In a second instance, it employs a physical drift estimation module to project the pathway of detected debris in space and time. These modules ingest Sentinel-2 and PlanetScope satellite time series as well as meteorological data to provide up-to-date, high-resolution estimates of the locations of floating objects. This is crucial for on-site ship-based cleanup efforts that rely on accurate and timely information on floating litter islands for efficient collection. Automating floating debris detection poses several challenges: the domain shift between the globally distributed satellite scenes is strong; the small footprint of debris induces a severely imbalanced dataset; a visual identification of debris is difficult even for experts; noisy and incomplete labels pose challenges to automated machine learning methods. The two-layered strategy tackles these issues, where the segmentation module is aided by a machine-learning-based tracking module. Addressing this real-world challenge in a physically sound manner will provide a unique opportunity to deploy methods beyond idealized benchmarks, and with ecological impact, providing cleanup missions with up-to-date and physically plausible marine litter location estimations.