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

TL;DR

This paper reviews how recent machine learning advances and satellite data can enhance automated cropland monitoring, discussing applications, challenges, and future prospects for societal impact.

Contribution

It provides a comprehensive overview of ML applications in cropland monitoring, highlighting recent progress, challenges, and future research directions.

Findings

ML approaches show promise in crop mapping

Satellite data enables large-scale, long-term monitoring

Major challenges remain in model robustness and data quality

Abstract

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.