Research on Key Technologies for High-Precision Land Cover Change Monitoring Using Satellite Data

The increasing availability of satellite remote sensing data has made automatic land cover change detection a persistent research focus. However, real-world applications show that single AI models struggle to cope with the combined challenges of spatial-temporal complexity, feature diversity, and evolving engineering requirements. Consequently, the accuracy of automatically extracted land cover changes is often compromised, making the results insufficient for direct engineering application. Guided by practical application need, this paper focuses on how to utilize satellite remote sensing data, various knowledge and AI technologies to improve the accuracy and efficiency of automatic land cover extraction. This research focuses on the key technologies involved in the complete land cover monitoring process. Central to this study is the proposal of a progressive intelligent change detection technology for satellite remote sensing, characterized by a “identify all, discriminate precisely, refine extraction” workflow. Specifically, the “identify all” step extracts all potential change patches using models such as generic binary change detection. Building on these results, the “discriminate precisely” step filters out patches that are not of current interest. Finally, the “refine extraction” step employs models like semantic segmentation to further screen the results and enhance overall accuracy. An application demonstration in Shanxi Province, China, for new PV facilities, buildings, and roads demonstrated a recall rate of 89.3% for automatic extraction. The high-quality outputs confirm the practical applicability of the results. Consequently, this research affirms the technology as both a valuable and transferable solution for land cover monitoring.