Climate science is critical for understanding both the causes and consequences of changes in global temperatures and has become imperative for decisive policy-making. However, climate science studies commonly require addressing complex interoperability issues between data, software, and experimental approaches from multiple fields. Scientific workflow systems provide unparalleled advantages to address these issues, including reproducibility of experiments, provenance capture, software reusability and knowledge sharing. In this paper, we introduce a novel workflow with a series of connected components to perform spatial data preparation, classification of satellite imagery with machine learning algorithms, and assessment of carbon stored by urban trees. To the best of our knowledge, this is the first study that estimates carbon storage for a region in Africa following the guidelines from the Intergovernmental Panel on Climate Change (IPCC).

中文翻译：

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城市树木碳储存评估的语义工作流和机器学习

气候科学对于理解全球温度变化的原因和后果至关重要，并且已成为决定性政策制定的必要条件。然而，气候科学研究通常需要解决来自多个领域的数据、软件和实验方法之间复杂的互操作性问题。科学工作流程系统为解决这些问题提供了无与伦比的优势，包括实验的可重复性、来源捕获、软件可重用性和知识共享。在本文中，我们介绍了一种具有一系列连接组件的新工作流程，用于执行空间数据准备、使用机器学习算法对卫星图像进行分类以及评估城市树木存储的碳。据我们所知，