# Geovisualization of Buildings: AI vs. Procedural Modeling
## 1 Introduction
Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are trained on large datasets to understand spatial relationships and architectural styles. These AI algorithms generate building layouts or floor plans based on learned patterns and semantic meanings derived from the input data [25; 26]. Moreover, online databases serve as valuable resources for pre-existing building elements, including 3D models, textures, and materials. APIs or web scraping techniques are utilized to access these databases, enabling the automatic retrieval and integration of relevant building components into the modeling pipeline [27; 28]. Machine learning techniques further optimize the procedural modeling process by iteratively refining generated designs based on user feedback and performance metrics. Reinforcement learning algorithms adaptively adjust model parameters to maximize design objectives, such as energy efficiency or structural integrity [29; 30]. Parametric modeling allows for the creation of flexible building models that can be dynamically adjusted based on various parameters such as size, style, and functionality. By defining parameters and constraints, designers can iteratively explore design alternatives and refine their concepts [31; 32]. Rule-based systems enforce design rules and constraints to ensure compliance with architectural guidelines and regulatory requirements. These systems detect errors and violations early in the design process, reducing costly revisions and ensuring adherence to legal mandates [33; 34]. Real-time feedback mechanisms enable designers to interactively manipulate building models and receive immediate visual feedback on design changes. Graphical user interfaces (GUIs) or virtual reality (VR) environments provide intuitive interfaces for exploring and refining designs in real time [35]. Simulation and visualization tools facilitate the evaluation and validation of building models through realistic rendering, lighting analysis, and virtual walkthroughs. By visualizing design outcomes in immersive 3D environments, stakeholders can gain insights into the spatial qualities and functional aspects of proposed buildings [36; 37; 38]. Lastly, scalability and adaptability are crucial aspects of automated procedural building modeling systems, allowing them to accommodate diverse project requirements and scales. Cloud-based infrastructure and modular software architectures enable the efficient handling of large-scale modeling tasks and foster interoperability and extensibility [39; 40]. Automated procedural building modeling represents a transformative approach to architectural design, leveraging advanced technologies to streamline the process, optimize resource utilization, and create sustainable built environments that enrich the lives of inhabitants and communities [41; 42; 43]. * Building Descriptions as Input: The initial step in automated procedural building modeling is gathering building descriptions. These descriptions may contain information such as the number of floors, types of materials, room layouts, and architectural elements. * Visual Recognition of Building Characteristics: Utilizing computer vision techniques and image processing allows systems to automatically recognize building characteristics from visual data. This may involve identifying shapes, colors, textures, and other key elements. * AI and Data Analysis: After collecting descriptions or visual recognition, artificial intelligence algorithms can analyze this data and generate building model plans. These AI systems can employ techniques like deep learning for precise understanding and interpretation of data. * Online Databases of Building Elements: Online databases containing a plethora of building elements, such as windows, doors, roofs, and more, serve as valuable resources. These databases can be integrated into the procedural modeling pipeline, allowing for the automatic selection and placement of elements based on the generated building plans.