Field Boundary

Accurately mapping agricultural field boundaries from satellite imagery is crucial for precision agriculture, yield prediction, and resource management, but remains challenging, especially in smallholder farming systems. Current research focuses on leveraging machine learning, particularly deep learning models like U-Net architectures and the Segment Anything Model (SAM), to automate this process, often employing transfer learning and techniques like pseudo-labeling to address the scarcity of labeled training data in many regions. These advancements are improving the accuracy and scalability of field boundary delineation, enabling more efficient monitoring and analysis of global agricultural practices. The resulting datasets and improved algorithms are significantly impacting agricultural research and informing policy decisions related to food security and sustainable land management.