# Geovisualization of Buildings: AI vs. Procedural Modeling
## 1 Introduction  
Procedural modeling stands out when compared to conventional methods of geovisualizing 3D building models due to its ability to encapsulate model descriptions, such as those of buildings or residences, using scripting languages or machine code. This feature renders procedural modeling exceptionally conducive to computational processing, facilitating the expeditious rendering of comprehensive building models and urban landscapes composed of homogeneous building structures. In essence, procedural modeling thrives particularly in scenarios where building diversity is relatively limited, thus accentuating its manifold advantages [1; 2; 3]. Procedural modeling is a technique that generates 3D models and environments algorithmically, as opposed to manually creating each element. This approach utilizes a set of rules or procedures to automatically create complex structures, allowing for the efficient generation of large-scale models with varying levels of detail. Procedural modeling isparticularly effective in urban modeling, where it can be used to create extensive cityscapes with minimal manual input [4]. This method is highly flexible and scalable, making it ideal for applications in virtual reality, video games, and urban planning. The conventional approach to geovisualizing 3D building models typically involves manual or semi-automated processes that rely on CAD (Computer-Aided Design) tools and GIS (Geographic Information Systems) software. This approach often requires significant manual intervention to create accurate and detailed 3D models from 2D plans, maps, or on-site measurements [5]. These models are then used in various applications, such as urban planning, architectural design, and geospatial analysis. While this approach can produce highly detailed models, it is time-consuming and less adaptable to changes in scale or design. The integration of AI-driven procedural modeling into architectural and urban design holds significant implications for the future of these fields. By automating the generation of detailed 3D building models, this research offers a transformative tool that can greatly enhance the efficiency and precision of design processes. Architects and urban planners can leverage these advanced techniques to quickly prototype and visualize complex structures, enabling more informed decision-making and facilitating a more iterative design process. Moreover, the ability to rapidly produce accurate and adaptable models supports the development of smart cities, where real-time data can be integrated into urban planning to create more responsive and sustainable environments. This research thus not only advances the technical capabilities of 3D modeling but also contributes to the broader goal of creating more efficient, livable, and resilient urban spaces. Procedural modeling stands out prominently in the field of geovisualizing 3D building models due to its efficiency in rendering comprehensive building models and urban landscapes. However, despite its advantages, there is a significant gap in the ability to accurately portray the diversity and complexity of real-world buildings [6]. This research aims to bridge this gap by integrating procedural modeling with advanced AI techniques, offering a robust solution for automated and accurate 3D building modeling. The motivation for this work stems from the need to enhance the geovisualization of buildings and automate the production process. Traditional methods often fall short in terms of efficiency and accuracy when dealing with diverse building structures [7; 8; 9]. By leveraging AI and machine learning, this research proposes a novel methodology to improve procedural modeling and address the existing limitations. In this research, the ambitious task of elucidating the intricate amalgamation of procedural modeling with novel techniques and tools will be undertaken, aimed at enhancing the geovisualization of building models and streamlining the manufacturing process.