Recently, there are attempts to expand the current usage of satellite Earth surface observation images to forward-looking applications to support decision-making and fast response against future natural hazards. Specifically, deep learning techniques were employed to synthesize Earth surface images at the pixel level. Those studies found that precipitation and soil moisture play non-trivial roles in Earth surface condition prediction tasks. However, unlike many well-defined and well-studied topics, such as change detection, for which many benchmark datasets are openly available, there are limited public datasets for the abovementioned topic for fast prototyping and comparison. To close this gap, we introduced a comprehensive dataset containing SAR images, precipitation, soil moisture, land cover, Height Above Nearest Drainage (HAND), DEM, and slope data collected during the 2019 Central US Flooding events. Deep-learning-based SAR image synthesis and flood mapping with the synthesized images were presented as sample use cases of the dataset.

中文翻译：

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EarthObsNet：用于数据驱动的地球观测图像合成的综合基准数据集


最近，有人试图将卫星地球表面观测图像的当前使用扩展到前瞻性应用程序，以支持针对未来自然灾害的决策和快速响应。具体来说，采用深度学习技术在像素级别合成地球表面图像。这些研究发现，降水和土壤水分在地球表面状况预测任务中起着重要作用。然而，与许多定义明确且研究充分的主题（例如变化检测）不同，许多基准数据集都是公开可用的，而上述主题的公共数据集有限，可用于快速原型设计和比较。为了缩小这一差距，我们引入了一个全面的数据集，其中包含 SAR 图像、降雨量、土壤湿度、土地覆盖、最近排水高度 （HAND）、DEM 以及 2019 年美国中部洪水事件期间收集的坡度数据。基于深度学习的 SAR 图像合成和合成图像的洪水映射作为数据集的示例用例。