MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data

urrently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. We provide annotations (georeferenced polygons/ pixels) from verified plastic debris events in several geographical regions globally, during different seasons, years and sea state conditions. A detailed spectral and statistical analysis of the MARIDA dataset is presented along with well-established ML baselines for weakly supervised semantic segmentation and multi-label classification tasks. MARIDA is an open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines.

目前，大量研究致力于通过遥感技术检测海洋垃圾并评估其光谱行为，旨在开发新的运营监测解决方案。在此，我们引入了海洋垃圾档案（MARIDA），作为开发与评估能够检测海洋垃圾的机器学习（ML）算法的基准数据集。MARIDA是首个基于多光谱Sentinel-2（S2）卫星数据的数据集，能够将海洋垃圾与共存的各种海洋特征区分开来，包括海藻、船只、自然有机物质、波浪、尾流、泡沫、不同类型的水（例如清澈水、浑浊水、含泥沙水、浅水）以及云层。我们提供了来自全球多个地理区域、不同季节、年份和海况条件下的经过验证的塑料垃圾事件的地理标记多边形/像素标注。本文详细介绍了MARIDA数据集的光谱和统计分析，以及用于弱监督语义分割和多标签分类任务的成熟的机器学习基线。MARIDA是一个开放获取的数据集，它使得研究界能够探究特定漂浮物质、海况特征和水型的光谱行为，开发与评估基于人工智能和深度学习架构的海洋垃圾检测解决方案，以及卫星预处理流程。