Abstract: With the rapid development of big data and artificial intelligence technologies, significant progress has been achieved in automatic interpretation techniques for remote sensing imagery. However, the robustness, reliability, and accuracy of existing automatic interpretation methods still fall short of human-level performance when compared. To address the practical production application needs, this research has developed a multi-object extraction model that establishes a hierarchical relationship among scene-object-pixel, thereby creating a comprehensive suite of technologies for remote sensing image classification and feature extraction. It introduces novel methods for change detection, such as semantic information enhancement coupled with false alarm re-suppression mechanisms, fusion of prior shape knowledge, optimized feature matching, and 2D-3D joint processing. Furthermore, it proposes a conceptual framework for interactive land feature extraction and editing through human-computer intelligent collaboration, thus constructing a high-performance intelligent interpretation technology system for remote sensing imagery. This research has resulted in the development of an independent intellectual property software system named EasyFeature. And it has been widely applied in major national projects such as global mapping and normalized monitoring of natural resources, effectively reducing China's reliance on foreign comparable software solutions.