# 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?
* Need for Large Datasets: training effective machine learning and artificial intelligence models requires large datasets, which can be challenging to gather and process, especially for specific locales or architectural styles. * Requirement for Computational Resources: some advanced techniques, such as Generative Adversarial Networks (GANs) or deep neural networks, require large amounts of computational resources for training and model generation, which can be costly and require specialized hardware infrastructure. * Challenges of Result Interpretation: in some cases, machine learning and artificial intelligence techniques may generate models that are difficult to interpret or explain, which can be problematic in certain applications, such as urban planning or architectural design. Despite these challenges, research in the field of automating the process of obtaining 3D building models continues to advance, and further work on improving algorithms, techniques, and the quality of input data may help overcome these drawbacks [49]. The impact of input variation on procedural modeling, which is the optimal input modality for AI-based procedural modeling, comparing verbal descriptions with optical recognition of building images, delves into the efficacy and suitability of these two distinct approaches in informing the generation of procedural models for 3D buildings. This research explores two primary approaches for automated procedural building modeling: verbal descriptions and optical recognition of building images. Verbal descriptions as input for 3D modeling involve utilizing textual narratives or structured data outlining the characteristics of buildings. Natural language processing (NLP) techniques are employed to parse and extract relevant information. For instance, descriptions of architectural styles, materials, and dimensions are translated into detailed 3D models. Optical recognition of building images DeepMind's Alpha3D Experimental was used. Computer vision algorithms analyze visual data from images or videos. Techniques such as convolutional neural networks (CNNs) are used (Midas is used because it can be highly effective when combined with other 3D reconstruction methods working with a single image like in our case) to identify and extract architectural features. This method leverages deep learning to generate accurate 3D models based on visual inputs. A comparative analysis is conducted to evaluate the efficiency of these approaches. Metrics such as model accuracy, computational efficiency, and user satisfaction are assessed. The findings will provide insights into the optimal methodology for different scenarios (Figure 1). Figure 1 flowchart explanation:  
* Collect Verbal Descriptions (gather textual descriptions of buildings, including architectural styles, dimensions, and materials). * Utilize NLP for Data Parsing (use Natural Language Processing (NLP) techniques to parse and extract relevant information from the collected verbal descriptions). * Generate Initial 3D Models (create initial 3D building models based on the parsed data). * Collect Building Images (gather images or videos of buildings from various sources). * Apply Computer Vision Algorithms (use computer vision techniques to analyze the collected images and extract architectural features). * Extract Architectural Features (identify and extract detailed architectural elements from the images). * Generate Detailed 3D Models (create detailed 3D models based on the extracted features). * Comparative Analysis of Methods (compare the models generated from verbal descriptions and images to evaluate their accuracy and efficiency). * Evaluate Model Accuracy and Efficiency (assess the models based on various metrics such as accuracy, fidelity, and computational efficiency). * Refine Models Based on Findings (make necessary adjustments to improve the models based on the evaluation results). This study's experimental design focuses on developing an AI-driven procedural modeling approach aimed at improving the geovisualization of 3D building models.