论文标题
海洋碎屑和浮动塑料的高精度密度映射通过卫星图像
High-precision Density Mapping of Marine Debris and Floating Plastics via Satellite Imagery
论文作者
论文摘要
将多光谱卫星数据和机器学习结合起来，是一种监测海洋环境中塑料污染物的方法。最近的研究取得了关于通过机器学习鉴定海洋塑料的理论进步。但是，尚无研究评估这些方法在映射和监测海洋塑料密度的应用。因此，本文由三个主要组成部分组成：（1）机器学习模型的开发，（2）MAP-MAPPER的构建，一种用于绘制海洋塑料密度的自动化工具，最后（3）（3）对整个系统的评估，以进行分布外测试地点。本文的发现利用了机器学习模型需要高精度来减少误报对结果的影响。开发的MAP-MAPPER架构提供了用户选择，以在培训/测试数据集中以高精度（$ \ textit {abbv。} $ -HP）或最佳的precision-recall（$ \ textit {abbv。} $ -OPT）值。我们的MAP-MAPPER-HP模型将塑料检测的精度大大提高到95 \％，而MAP-MAPPER-OPT达到了87 \％-88 \％的Precision-Recall对。地图映射器用第一个利用先进的深度/机器学习和多光谱图像来映射自动化软件中的海洋塑料密度的工具为文献做出了贡献。提出的数据管道采用了一种新的方法来绘制海洋地区的塑性密度。因此，这可以对这种方法的挑战和机会进行初步评估，以帮助指导未来的工作和科学研究。
Combining multi-spectral satellite data and machine learning has been suggested as a method for monitoring plastic pollutants in the ocean environment. Recent studies have made theoretical progress regarding the identification of marine plastic via machine learning. However, no study has assessed the application of these methods for mapping and monitoring marine-plastic density. As such, this paper comprised of three main components: (1) the development of a machine learning model, (2) the construction of the MAP-Mapper, an automated tool for mapping marine-plastic density, and finally (3) an evaluation of the whole system for out-of-distribution test locations. The findings from this paper leverage the fact that machine learning models need to be high-precision to reduce the impact of false positives on results. The developed MAP-Mapper architectures provide users choices to reach high-precision ($\textit{abbv.}$ -HP) or optimum precision-recall ($\textit{abbv.}$ -Opt) values in terms of the training/test data set. Our MAP-Mapper-HP model greatly increased the precision of plastic detection to 95\%, whilst MAP-Mapper-Opt reaches precision-recall pair of 87\%-88\%. The MAP-Mapper contributes to the literature with the first tool to exploit advanced deep/machine learning and multi-spectral imagery to map marine-plastic density in automated software. The proposed data pipeline has taken a novel approach to map plastic density in ocean regions. As such, this enables an initial assessment of the challenges and opportunities of this method to help guide future work and scientific study.
