Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection
Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection
复制标题
DOI：
10.1175/bams-d-19-0324.1
复制
发表时间：
2020-11-01
影响因子：
8
通讯作者：
Stevens, Bjorn
中科院分区：
文献类型：
--
作者：
Rasp, Stephan;Schulz, Hauke;Stevens, Bjorn
人类擅长检测图像中有趣的模式，例如从卫星拍摄的图像。这种轶事证据可以导致新现象的发现。然而，通常很难收集足够的主观特征数据来进行重要分析。本文提供了一个示例，说明如何将众包和深度学习这两种最近可供广泛研究人员使用的工具结合起来大规模探索卫星图像。特别是，重点是信风地区浅积云对流的组织。浅层云在地球辐射平衡中发挥着重要作用，但在气候模型中却很少得到体现。对于这个项目，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，该数据集用作深度学习算法的训练数据集，使模式检测自动化并创建四种模式的全球气候学成为可能。对地理分布和大范围环境条件的分析表明，这四种模式与既定的组织模式有一些重叠，例如开放和封闭的细胞对流，但也有重要的不同。该项目的结果和数据集提出了有前景的研究问题。此外，这项研究表明，众包和深度学习在图像数据集的探索方面可以很好地相辅相成。
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, crowdsourcing 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 crowdsourcing 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 suggest promising research questions. Further, this study illustrates that crowdsourcing and deep learning complement each other well for the exploration of image datasets.