Cropland Mapping

Cropland mapping uses remote sensing data and machine learning to create accurate maps of agricultural land, crucial for monitoring food security, agricultural development, and environmental management. Current research emphasizes improving mapping accuracy in data-scarce regions, particularly in Africa, by leveraging techniques like weak supervision, incorporating readily available global datasets to augment limited ground truth data, and employing deep learning models such as LSTMs and Random Forests, as well as visual foundation models with prompt learning. These advancements enhance the precision and accessibility of cropland maps, supporting improved agricultural planning, resource allocation, and environmental monitoring efforts globally.