As an Assistant Researcher at the Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, my academic interests center on several interconnected domains at the forefront of artificial intelligence and remote sensing technologies. Intelligent interpretation of remote sensing targets represents a rapidly evolving field that combines computer vision, machine learning, and Earth observation sciences. This domain focuses on developing automated systems capable of extracting meaningful information from satellite and aerial imagery, enabling applications ranging from environmental monitoring to urban planning and disaster management. Agentic AI emerges as a transformative paradigm where autonomous agents demonstrate sophisticated reasoning, planning, and decision-making capabilities. This field explores how artificial systems can operate independently while maintaining goal-directed behavior in complex, dynamic environments. Large language models have revolutionized natural language processing and are increasingly being integrated into multimodal applications. These models demonstrate remarkable capabilities in understanding, reasoning, and generating human-like responses across diverse tasks and domains. Meta-learning, or "learning to learn," addresses the fundamental challenge of enabling AI systems to rapidly adapt to new tasks with minimal training data. This field is particularly relevant for scenarios where labeled data is scarce or expensive to ob