Land Cover Mapping in Limited Labels Scenario: A Survey

Supervised classification methods, especially recent deep learning approaches, have achieved significant success in commercial applications in Natural Language Processing (NLP) and Computer Vision (CV) domain, where large training data is available. Supervised machine learning algorithms, e.g., advanced deep neural networks, require sufficient labeled training instances which are representative of the test data. Such training data is often scarce in land cover applications given high manual labor and material cost required in manual labeling (e.g., visual inspection) and field study. This is further exacerbated by the high-dimensional nature of spatio-temporal remote sensing data. Moreover, land covers commonly show much variability across space and time, e.g., the same crop can look different in different years and in different regions due to variability in weather conditions and farming practice. Additionally, the availability of multiple RS data sources acquired at different spatial and temporal resolutions, and other heterogeneous data, e.g., elevation, thermal anomalies, and night-time light intensity, provides unique algorithmic challenges that need to be addressed.