A Cloud-Based Framework for Large-Scale Monitoring of Ocean Plastics Using Multi-Spectral Satellite Imagery and Generative Adversarial Network

Marine debris is considered a threat to the inhabitants, as well marine environments. Accumulation of debris, besides climate change factors, including warming water, sea-level rise, and changes in oceans’ chemistry, are causing potential collapse environment’s health. Due increase plastics coastlines, ocean sea surfaces, even deep layers, there need for developing new advanced technology detection large-sized pollution (with sizes larger than 1 m) using state-of-the-art remote sensing machine learning tools. Therefore, we developed cloud-based framework large-scale with integration Sentinel-2 satellite imagery tools on Sentinel Hub cloud application programming interface (API). Moreover, evaluated performance two shallow algorithms random forest (RF) support vector (SVM), method generative adversarial network-random (GAN-RF) pilot site Mytilene Island, Greece. Based obtained results, RF SVM achieved an overall accuracy 88% 84%, respectively, available training data plastic debris. The GAN-RF classifier improved by 8%, achieving 96% generating several synthetic samples.