# Geovisualization of Buildings: AI vs. Procedural Modeling
## 3 Comparative Analysis, Results, and Discussion
### Final Results and Discussion  
The comparative analysis reveals that the AI-based approach using verbal descriptions provides a structured and efficient method for generating 3D models. However, it lacks the detailed accuracy of models generated through optical recognition. The latter approach excels in capturing intricate architectural features but requires high-quality visual data and significant computational resources. We also bring discussions and the contribution of our research with reference to relevant recent literature. The key contribution of this research lies in its AI-driven procedural automation, which enhances modeling accuracy and efficiency at an individual building level. Meanwhile, Ref. [53] focuses on reducing the computational overhead in large-scale urban environments. Together, these works highlight the potential for integrating procedural modeling with lightweight reconstruction methods to improve both the efficiency and fidelity of urban 3D visualizations, especially for smart city applications. Our research significantly contributes to the advancements of 3D city modeling techniques that were explored in another study [54]. In both papers, procedural modeling emerges as an essential method for efficiently generating detailed urban structures, but the integration of AI, as we showed, enhances accuracy and scalability. In Ref. [54], procedural modeling is highlighted for its ability to quickly generate complex 3D city models, suitable for large-scale urban planning projects and simulations. However, the limitations regarding manual adjustments, model diversity, and texture accuracy are evident. The AI-driven approaches from our research address these limitations by employing machine learning techniques such as convolutional neural networks (CNNs) and Generative Adversarial Networks (GANs) for tasks such as image-to-geometry translation and texture generation. Thus, the AI integration proposed in this research not only streamlines the generation of diverse building models but also provides higher realism, reducing the manual effort required in traditional procedural methods, and ultimately aligning with the goals outlined in Ref. [54]. This fusion of procedural and AI techniques presents a transformative approach for urban planners and architects, offering enhanced visualization, efficiency, and adaptability in 3D smart city modeling. We also showed the enhancement of procedural modeling approaches discussed in the literature and in Ref. [55] by integrating AI-driven methods. While the digital twin research focuses on workflows for large-scale 3D urban models, AI-based procedural modeling optimizes the generation of complex building structures and textures with greater efficiency. The AI techniques, particularly CNNs and GANs, enable more realistic, dynamic models, addressing limitations in traditional procedural approaches and facilitating adaptable, high-detail urban simulations. The web-based application discussed in Ref. [56] by advancing procedural modeling with AI integration is very interesting. While the web-based tool focuses on efficient 3 DCM (3D Composite Manufacturing) management, tiling, and spatial analysis, AI-enhanced procedural modeling allows for more detailed and accurate 3D building generation from 2D data. This AI-driven approach improves the efficiency and precision of 3 DCM creation, aligning with the goal of optimizing model generation and visualization in web environments. Our research also contributes to the advancements in procedural modeling discussed in Ref. [57] by incorporating AI to enhance model automation. While Ref. [57] focused on procedural generation through CGA-based workflows and traditional shape grammars, the AI-driven techniques that we showed here improve the accuracy and efficiency of generating diverse, complex building models. This AI integration aligns with Ref. [57]'s goal of automating design, boosting creativity, and offering flexible adjustments in urban design.