Applying AI for Improved Detection of Debris in Dumpsites and Floating Matter via Spatio-Temporal Fusion of Sentinel-2 and PlanetScope Satellite Data

A significant portion of plastic floating on the ocean surface originates from land, often transported by rivers. A large share of this debris leaks from dumpsites located near rivers, especially during storms or similar events, and eventually reaches the ocean. In this paper, Machine Learning (ML) methods are used to track waste in dumpsites near waterways and classify floating debris accumulations on the ocean surface using data from spatio-temporal fusion of Sentinel-2 and Planet Scope. The study involves two case studies: the first focuses on monitoring dumpsite evolution using both ML standard pixel-based classification approaches and Deep Learning (DL) methods, while the second addresses the classification of floating matter on the ocean surface using a pre-trained algorithm on the Sentinel-2 data and adapted to Sentinel-2/Planet fused image. The goal of this study is to improve the temporal accuracy of debris detection and contribute to environmental conservation through the integration of AI and satellite data fusion techniques.