Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning
It addresses the problem of efficient and scalable agricultural monitoring for societal benefit, but is incremental as it synthesizes existing advances.
The paper reviews how machine learning and satellite data can enhance automated cropland mapping over large areas and time, highlighting promising applications and recent results in crop monitoring.
This paper 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 period and over large regions. It discusses three applications in the domain of crop monitoring where ML approaches are beginning to show great promise. For each application, it highlights machine learning challenges, proposed approaches, and recent results. The paper 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.