Warburton Pierce, Shuler Kurtis, Zenker Jake P, Patel Lekha
Scientific Machine Learning, Sandia National Laboratories, Albuquerque, NM, 87123, US.
Statistical Sciences, Sandia National Laboratories, Albuquerque, NM, 87123, US.
Sci Data. 2025 Jun 21;12(1):1060. doi: 10.1038/s41597-025-04911-2.
Ship tracks, long thin artificial cloud features formed from the pollutants in ship exhaust, are satellite-observable examples of aerosol-cloud interactions (ACI) that can lead to increased cloud albedo and thus increased solar reflectivity, phenomena of interest in solar radiation management. In addition to ship tracks being of interest to meteorologists and policy makers, their observed cloud perturbations provide benchmark evidence of ACI that remain poorly captured by climate models. To broadly analyze the effects of ship tracks, high-resolution satellite imagery data highlighting their presence are required. To support this, we provide a hand labelled dataset to serve as a benchmark for a variety of subsequent analyses. Established from a previous dataset that identified ship track presence using NASA's MODIS Aqua satellite imager, our first-of-its-kind dataset is comprised of image masks: capturing full ship track regions, including their contours, emission points and dispersive patterns. In total, 300 images, or around 2,500 masked ship tracks, observed under varying conditions are provided, and may facilitate training of machine learning algorithms to automate extraction.
船迹是由船舶废气中的污染物形成的细长人工云特征,是气溶胶-云相互作用(ACI)的卫星可观测示例,这种相互作用会导致云反照率增加,从而提高太阳反射率,这是太阳辐射管理中感兴趣的现象。除了气象学家和政策制定者对船迹感兴趣外,它们所观测到的云扰动为气溶胶-云相互作用提供了基准证据,而气候模型对这些证据的捕捉仍然很差。为了广泛分析船迹的影响,需要突出其存在的高分辨率卫星图像数据。为此,我们提供了一个人工标注的数据集,作为后续各种分析的基准。我们这个同类首个数据集是根据之前使用美国国家航空航天局(NASA)的中分辨率成像光谱仪(MODIS)Aqua卫星成像仪识别船迹存在的数据集建立的,由图像掩码组成:捕捉完整的船迹区域,包括其轮廓、排放点和扩散模式。总共提供了在不同条件下观测到的300张图像,或约2500个带掩码的船迹,这可能有助于训练机器学习算法以实现自动提取。