Abstract: Objectives: In recent years, 3D spatiotemporal information has become a cornerstone of national new infrastructure construction, with airborne point clouds playing a critical role in large-scale 3D data acquisition. However, effectively interpreting these point clouds and extracting their semantic richness remain significant challenges. Traditional methods, including geometry-driven feature extraction and data-driven deep learning models, have achieved limited progress but struggle with the complexity and scale of airborne point clouds. Advances in large-scale foundation models (FMs), particularly in natural language processing and vision-language integration, offer new opportunities for point cloud understanding. Large language models (LLMs) exhibit exceptional generalization and cross-modal semantic capabilities, enabling a “ languagecentered” paradigm. By leveraging language models to map point cloud data into a semantically enriched space, this approach addresses limitations of traditional methods. Methods: The evolution of point cloud understanding is examined across geometry- and data-driven approaches, self-supervised paradigms, and FMdriven methodologies. A "language-centered" framework for airborne point cloud understanding is proposed, tackling high-level semantic modeling, cross-modal alignment, and downstream task adaptation. Results demonstrate enhanced semantic representation, improved generalization, and significant advantages in complex scenarios. Results: The findings provide new insights into point cloud understanding and establish a foundation for integrating large-scale models into 3D applications. Conclusions: These contributions offer innovative perspectives and technical solutions for advancing point cloud technologies in national infrastructure projects.