挖掘大气数据
Mining atmospheric data
摘要：
本文概述了两个相互依存的问题，重要的挖掘遥感数据（如图像）从大气监测任务。第一个问题涉及建立新的公共数据集和基准，这是遥感界的优先事项。第二个问题是大气数据分类的深度学习方法的研究，该方法基于大量的无注释数据和由稀疏的地面观测网络提供的本地化注释数据。目标应用是空气质量评估和预测。空气质量被定义为与气体和气溶胶等几种大气成分相关的污染水平。空气污染引起的空气质量恶化与公众健康之间存在依赖关系。目标应用是开发一个快速预测模型，用于本地和区域空气质量评估和跟踪。数据挖掘的结果将对公民和决策者产生重大影响，因为它提供了一个快速预测和可靠的空气质量监测系统，能够通过对稀疏的地面现场测量网络进行智能外推，覆盖当地和区域范围。
英文摘要：
This paper overviews two interdependent issues important for mining remote sensing data (e.g. images) obtained from atmospheric monitoring missions. The first issue relates the building new public datasets and benchmarks, which are hot priority of the remote sensing community. The second issue is the investigation of deep learning methodologies for atmospheric data classification based on vast amount of data without annotations and with localized annotated data provided by sparse observing networks at the surface. The targeted application is air quality assessment and prediction. Air quality is defined as the pollution level linked with several atmospheric constituents such as gases and aerosols. There are dependency relationships between the bad air quality, caused by air pollution, and the public health. The target application is the development of a fast prediction model for local and regional air quality assessment and tracking. The results of mining data will have significant implication for citizen and decision makers by providing a fast prediction and reliable air quality monitoring system able to cover the local and regional scale through intelligent extrapolation of sparse ground-based in situ measurement networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
