Algorithms for Synthesizing 3D Spatial Models Based on 2D Visual Representations of Buildings and Analyzing Their Aerodynamic, Insolation, and Energy Efficiency

This study elucidates a methodology for autonomously reconstructing the 3D spatial geometry of buildings from limited 2D static images to execute advanced climatic simulations. By integrating deep learning with computational fluid dynamics, solar radiation, wind aerodynamics, and energy consumption were analyzed. Empirical observations confirmed that AI-formulated topological data possess high validity in predicting microclimates (r = 0.94). The conceptual findings provide an automated express-diagnostic platform for optimizing energy consumption in urban planning.

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