论文标题
关于土地分类的大地观测数据的语义
On the semantics of big Earth observation data for land classification
论文作者
论文摘要
本文讨论了将大地观测数据用于土地分类的挑战。采用的方法是考虑纯数据驱动的方法不足以表示连续变化。在使用大数据时，我们主张声音理论。在修改了诸如FAO的土地覆盖分类系统（LCC）之类的现有分类方案后，我们得出结论，LCC和类似的建议无法捕获景观动态的复杂性。然后，我们研究用于分析卫星图像时间序列的概念；我们将这些概念显示为事件的实例。因此，为了持续监视土地变化，事件识别需要替换对象识别作为现行范式。本文结束了结论，展示事件语义如何改善数据驱动的方法以实现大数据的潜力。
This paper discusses the challenges of using big Earth observation data for land classification. The approach taken is to consider pure data-driven methods to be insufficient to represent continuous change. We argue for sound theories when working with big data. After revising existing classification schemes such as FAO's Land Cover Classification System (LCCS), we conclude that LCCS and similar proposals cannot capture the complexity of landscape dynamics. We then investigate concepts that are being used for analyzing satellite image time series; we show these concepts to be instances of events. Therefore, for continuous monitoring of land change, event recognition needs to replace object identification as the prevailing paradigm. The paper concludes by showing how event semantics can improve data-driven methods to fulfil the potential of big data.
