Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale
综述了机器学习与地球观测卫星数据如何提升大规模、长时期农田自动制图能力，讨论了三个作物监测应用中的机器学习挑战、方法及成果，并指出实现其社会价值需解决的主要难题。
Abstract This article provides an overview of how recent advances in machine learning and the availability of data from earth observing satellites can dramatically improve our ability to automatically map croplands over long periods and over large regions. It discusses three applications in the domain of crop monitoring where machine learning (ML) approaches are beginning to show great promise. For each application, it highlights machine learning challenges, proposed approaches, and recent results. The article concludes with discussion of major challenges that need to be addressed before ML approaches will reach their full potential for this problem of great societal relevance.