  Land cover mapping is essential for monitoring global environmental change
and managing natural resources. Unfortunately, traditional classification
models are plagued by limited training data available in existing land cover
products and data heterogeneity over space and time. In this survey, we provide
a structured and comprehensive overview of challenges in land cover mapping and
machine learning methods used to address these problems. We also discuss the
gaps and opportunities that exist for advancing research in this promising
direction.
