Abstract: The rapid and accurate extraction of cultivated land is essential for supporting the protection of cultivated land and controlling cultivated land use. With the rapid development of high-resolution remote sensing and artificial intelligence technology, high-resolution cultivated land extraction has gradually transitioned from traditional pixel-based and object-oriented classification algorithms to intelligent cultivated land extraction represented by deep learning. Although many achievements have been made, the new technologies still face significant challenges. First, we sort out and analyze the research status of cultivated land extraction based on traditional machine-learning approaches and deep-learning techniques, which expounds on the necessity in cultivated land extraction. Second, we introduce the basic principle of deep semantic segmentation technology and the experimental process of cultivated land extraction, and summarize the state-of-the-art intelligent cultivated land extraction algorithms. Finally, focusing on some shortcomings of intelligent cultivated land extraction, the development trend of intelligent cultivated land extraction is discussed.