TRACE: An approach to detect, track and monitor large floating marine litter in our seas

Marine debris is a severe environmental problem. It originates from many sources and causes a wide spectrum of environmental, economic, safety, health and cultural impacts. Millions of tonnes enter the oceans every year and tackling the issue is gaining momentum at all levels.

Our project presents an approach to detect and track floating marine debris such as lost fishing nets, debris aggregations or patches based on high-resolution remote sensing time series, machine learning, and ocean current modeling. The goal is to obtain more reliable data regarding quantity, position, material properties and sources of litter as well as to address the lack of understanding of floating debris behavior in the open sea due to the limited monitoring capabilities (Garaba and Dierssen, 2018). In order to achieve this goal, a convolutional neural network was trained with a hand-labeled dataset of objects floating on the sea surface extracted from Planet satellite imagery, to learn the spatial characteristics of these objects. A software pipeline has been developed to automatically retrieve, process and analyze large amounts of satellite images and enable continuous monitoring. As identifying floating objects in the marine environment from space remains a challenging and difficult task and ground truthing is near to impossible, we further apply a mechanism to find matching objects on time series of satellite images using an ocean current model as well as different image processing techniques.