During the last decades, massive amounts of satellite images are becoming available that can be enriched with semantic annotations for the creation of value-added earth observation products. One challenge is to extract knowledge from the raw satellite data in an automated way and to effectively manage the extracted information in a semantic way, to allow fast and accurate decisions of spatiotemporal nature in a real operational scenario. In this work, we present a framework that combines supervised learning for crop type classification on satellite imagery time-series with semantic web and linked data technologies to assist in the implementation of rule sets by the European common agricultural policy (CAP). The framework collects georeferenced data that are available online and satellite images from the Sentinel-2 mission. We analyze image time-series that cover the entire cultivation period and link each parcel with a specific crop. On top of that, we introduce a semantic layer to facilitate a knowledge-driven management of the available information, capitalizing on ontologies for knowledge representation and semantic rules, to identify possible farmers noncompliance according to the Greening 1 (crop diversification) and SMR 1 rule (protection of waters against pollution caused by nitrates) rules of the CAP. Experiments show the effectiveness of the proposed integrated approach in three different scenarios for crop type monitoring and consistency checking for noncompliance to the CAP rules: the smart sampling of on-the-spot checks; the automatic detection of CAP's Greening 1 rule; and the automatic detection of susceptible parcels according to the CAP's SMR 1 rule.

中文翻译：

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语义丰富的作物类型分类和关联地球观测数据，以支持共同农业政策监测

在过去的几十年里，大量的卫星图像变得可用，这些图像可以通过语义注释来丰富，以创建增值地球观测产品。一个挑战是以自动化方式从原始卫星数据中提取知识，并以语义方式有效管理提取的信息，以便在实际操作场景中快速准确地做出时空性质的决策。在这项工作中，我们提出了一个框架，该框架将卫星图像时间序列作物类型分类的监督学习与语义网络和链接数据技术相结合，以帮助欧洲共同农业政策 (CAP) 实施规则集。该框架从 Sentinel-2 任务收集可在线获取的地理参考数据和卫星图像。我们分析覆盖整个栽培期的图像时间序列，并将每个地块与特定作物联系起来。最重要的是，我们引入了一个语义层，以促进可用信息的知识驱动管理，利用知识表示和语义规则的本体，根据绿化 1（作物多样化）和 SMR 1 规则识别可能的农民不合规（保护水域免受硝酸盐造成的污染）CAP 规则。实验表明，所提出的综合方法在作物类型监测和一致性检查不符合 CAP 规则的三种不同场景中的有效性：现场检查的智能抽样；自动检测CAP的Greening 1规则；以及根据 CAP 自动检测易受影响的包裹'