Architecture Engineers Award

  The rapid advancement of artificial intelligence , particularly large language models (LLMs), has opened new possibilities for comprehension, reasoning, and creative exploration across disciplines. In architecture and urban studies, point cloud data has become a critical digital resource due to its capacity to capture precise spatial and geometric information. This study addresses the gap between existing point cloud analysis methods and their limited applicability to direct scene design and semantic understanding, proposing a novel AI-driven framework to bridge this divide. Limitations of Current Point Cloud Applications Although prior research has extensively explored point cloud data for tasks such as semantic segmentation and target detection, the perceptual outputs of these approaches are often difficult to translate into meaningful design actions. The lack of semantic richness and contextual reasoning constrains their usability in architectural scene interpretation and cr...