结合众包和深度学习探索浅层对流的中尺度组织
Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection
摘要：
人类擅长在图像中发现有趣的模式，例如从卫星上拍摄的图像。这种轶事证据可以导致新现象的发现。然而，通常难以收集足够的主观特征数据进行显著性分析。本文介绍了一个例子，说明最近广泛的研究人员可以使用的两种工具，即众包和深度学习，可以结合起来大规模探索卫星图像。特别是，重点是在信风区的浅积云对流的组织。浅云在地球的辐射平衡中起着重要作用，但在气候模式中却表现得很差。在这个项目中，定义了四种主观的组织模式：糖、花、鱼和砾石。在两个研究所的云标记日，67名科学家在一个众包平台上筛选了10，000张卫星图像，并对近50，000个中尺度云团进行了分类。然后将该数据集用作深度学习算法的训练数据集，从而可以自动进行模式检测并创建四种模式的全球气候。对地理分布和大规模环境条件的分析表明，这四种模式与已建立的组织模式（如开放和封闭的细胞对流）有一些重叠，但在重要方面也有不同。该项目的结果和数据集提出了有前途的研究问题。此外，这项研究表明，众包和深度学习在图像数据集的探索中相互补充。
英文摘要：
Humans excel at detecting interesting patterns in images, for example those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features for significant analysis. This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowd-sourcing and deep learning, can be combined to explore satellite imagery at scale. In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions. Shallow clouds play a large role in the Earth's radiation balance yet are poorly represented in climate models. For this project four subjective patterns of organization were defined: Sugar, Flower, Fish and Gravel. On cloud labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowd-sourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggests promising research questions. Further, this study illustrates that crowd-sourcing and deep learning complement each other well for the exploration of image datasets.
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