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This paper provides an overview of how recent advances in machine learningand the availability of data from earth observing satellites can dramaticallyimprove our ability to automatically map croplands over long period and overlarge regions. It discusses three applications in the domain of crop monitoringwhere ML approaches are beginning to show great promise. For each application,it highlights machine learning challenges, proposed approaches, and recentresults. The paper concludes with discussion of major challenges that need tobe addressed before ML approaches will reach their full potential for thisproblem of great societal relevance.

Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning