TECHNOLOGIES FOR TRANSFORMING 2D IMAGES OF BUILDINGS INTO 3D MODELS USING GENERATIVE ARTIFICIAL INTELLIGENCE ALGORITHMS ON THE EXAMPLE OF THE NEW TASHKENT PROJECT (Multidisciplinary Journal of Science and Technology, 2026)
In modern urbanization processes, the automation of architectural visualization and the creation of digital twins have become primary factors in developing technological infrastructure. Within the framework of this research, a methodology was developed for automatically converting two-dimensional static images into three-dimensional digital mock-ups using deep learning architectures. The proposed solution is based on the synthesis of neural networks and generative models, allowing for the highly accurate reconstruction of the spatial dimensions and textural characteristics of objects. The dynamics of the obtained results demonstrated a sharp increase in the processing speed of architectural blueprints, alongside the optimization of computational resources for visual data analysis and volumetric modeling algorithms. The conceptual conclusions of the study present a practical platform aimed at reducing design costs and facilitating the formation of a digital urban cadastre through the widespread integration of artificial intelligence into urban planning information systems.