# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
###### Abstract  
The spatial and temporal coverage of spaceborne optical imaging systems are well suited for automated marine litter monitoring. However, developing machine learning-based detection and identification algorithms requires large amounts of data. Indeed, when it comes to marine debris, ground validated data is scarce. In this study, we propose a general methodology that leverages synthetic data in order to avoid overfitting and generalizes well. The idea is to utilize realistic models of spaceborne optical image acquisition and marine litter to generate large amounts of data to train the machine learning algorithms. These can then be used to detect marine pollution automatically on real satellite images. The main contribution of our study is showing that algorithms trained on simulated data can be successfully transferred to real-life situations. We present the general components of our framework, our modeling of satellites and marine debris and a proof of concept implementation for macro-plastic detection with Sentinel-2 images. In this case study, we generated a large dataset (more than 16,000 pixels of marine debris) composed of seawater, plastic, and wood and trained a Random Forest classifier on it. This classifier, when tested on real satellite images, successfully discriminates marine litter from seawater, thus proving the effectiveness of our approach and paving the way for machine learning-based marine litter detection with even more representative simulation models. p 2022146  
all continental land surfaces, all coastal waters up to 20 km from the shore and all closed seas such as the Mediterranean [7]. All these features make it an interesting tool for the detection of floating plastics in coastal waters compared to most of the available sensors which provide images that either have a poor resolution, a low revisit time or are not freely and openly distributed. A first requirement to detect plastic using satellites such as S2 is to collect data that we know contains plastic. This first step, though it seems basic, is quite challenging since it requires ground validation. Out of the studies that have been conducted, two trends have emerged regarding this matter, while [PERSON] et al. [8] and [PERSON] et al. [9] rely on ground truth events reported by citizen scientists, media, and social media, [PERSON] et al. [10] and [PERSON] et al. [11] rely on controlled in situ validation. In 2018, [PERSON] et al. [10] have developed large \(10\,\mathrm{m}\times 10\,\mathrm{m}\) artificial plastic targets and deployed them in the Aegean sea. They collected spectral data of these targets both from S2 and Unmanned Aerial Vehicles (UAV). These campaigns, collectively called the Plastic Litter Projects (PLP) have been conducted each year since 2018. They have provided the first in situ estimates of the spectral signatures of floating macro-plastics [10; 12]. This work inspired [PERSON] et al. [11] to deploy their own plastic target on the coast of Cyprus and thus to collect more in situ data. The large sizes considered for these targets are motivated by the resolution of the satellite: the S2 bands with the best resolution have a GSD of 10 m. Although these large patches are not necessarily representative of floating plastics in the open ocean, these controlled experiments provide exact descriptions of constituents as well as locations and dates of the patches, thus enabling a reliable collection of data. This method ensures a higher degree of certainty than relying on third-party reporting, as in [8; 9]. Another data source that has become more and more used in recent years is data from UAVs. For example, [PERSON] et al. [13] have used UAVs equipped with Thermal Infrared (TIR) cameras to detect floating macro plastics. They also showed how TIR data can be used to supplement data acquired in the visible and short-wave infrared ranges. UAVs have also been successfully used to detect debris on beaches [14; 15], rivers [16; 17], and to track the evolution of debris patches [18].