# Geovisualization of Buildings: AI vs. Procedural Modeling
## 2 Materials and Methods
### What Can Be Achieved with Today's Technology and Also by Combining Different Technologies?
Two primary approaches were explored: generating 3D models from verbal descriptions and creating models based on optical recognition of building images. These approaches were  
Figure 1: The research framework methodology and comparative analysis of two research patterns— by collecting verbal descriptions and collecting building images. subjected to a comparative analysis to evaluate their respective strengths and weaknesses in terms of model accuracy, computational efficiency, and user satisfaction. Regarding dataset selection for this study, we selected two types of datasets that cater to the different approaches. The first dataset comprises textual descriptions of various buildings, detailing architectural styles, materials, dimensions, and other key elements. This dataset was crucial for training the NLP models. The second dataset includes images and videos of buildings, sourced from architectural databases and on-site photography. These visual datasets were utilized to train computer vision models to accurately identify and extract architectural features for 3D model generation. The training process of the AI model was conducted in two parallel streams to address both the verbal and visual approaches. The NLP models were trained using the textual descriptions dataset, employing techniques such as tokenization and parsing to convert textual inputs into structured data. These models learned to interpret architectural terminology and generate corresponding 3D models. Concurrently, deep learning models, particularly convolutional neural networks (CNNs), were trained on the visual dataset. These models were designed to recognize architectural features from images, such as windows, doors, and structural elements, which were then used to generate detailed 3D models. Both models underwent iterative training, guided by performance metrics to ensure optimal accuracy and efficiency. To validate the effectiveness of the AI models, we employed several evaluation indicators. Model accuracy was assessed by comparing the AI-generated 3D models against manually created or real-world counterparts, focusing on metrics such as precision, recall, and F1-score. Computational efficiency was measured by the time required to generate 3D models from both verbal descriptions and images, alongside the computational resources utilized, including CPU and GPU usage. Additionally, user satisfaction was gauged through surveys and expert reviews, which evaluated the usability, interpretability, and overall quality of the generated models. Lastly, scalability was considered by examining the models' ability to handle varying levels of detail, from individual buildings to complex cityscapes. This comprehensive experimental design and methodology underscore the rigor of our research process, ensuring that the findings are both reliable and reproducible across similar studies. To validate each research hypothesis in this paper effectively, it is essential to employ specific technical methods and a carefully designed experimental framework. This approach will enhance the logical coherence of the paper by ensuring that each hypothesis is rigorously tested and supported by empirical evidence. For the first hypothesis, which posits that buildings in the real world are mostly different but can still be effectively modeled through procedural modeling, the validation process should begin by developing a procedural modeling framework capable of capturing the diversity inherent in real-world architecture. This can be accomplished by creating a flexible grammatical language designed to describe various building styles, architectural features, and structural elements. The experimental design should involve selecting a diverse dataset of building types that represent a wide range of architectural styles. By using this dataset, the framework can generate 3D models that are then compared against the original buildings. The validation of this hypothesis will be based on several metrics, including structural accuracy, aesthetic similarity, and computational efficiency.