# 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? There are not many ideas or research published in scientific journals focusing on procedural modeling, machine learning, and AI for 3D building modeling. The following are some examples of techniques and methods applied here. Procedural Modeling: Various techniques of procedural modeling are used to generate complex 3D building models. This includes generating parametric rules or scripts that automatically create detailed architectural structures. Published research often explores different approaches to procedural generation, including L-systems, shape grammars, or parameterized models. Parametric Modeling: this defines building elements using parameters such as height, width, and style, which can be adjusted to generate variations. Shape Grammar: formal grammar that defines rules for generating building shapes and styles based on hierarchical compositions of architectural elements. Procedural Texturing: generating textures for building surfaces based on procedural algorithms, such as fractal patterns or procedural noise. Machine Learning: In the field of machine learning, researchers apply various techniques to teach models to generate 3D building geometry from images or other input data. This can involve using convolutional neural networks (CNNs) for directly generating 3D models from images or using Generative Adversarial Networks (GANs) for generating textures or detailed features. Image-to-Geometry Translation: this trains models to convert 2D images of buildings into 3D geometric representations using techniques like convolutional neural networks (CNNs). Generative Models: these use generative models like Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs) to create plausible 3D building structures and textures from noise vectors or latent representations. Semantic Segmentation: this trains models to segment building elements (e.g., walls, windows, doors) in images, enabling more accurate reconstruction of 3D building geometry. Artificial Intelligence: In the realm of artificial intelligence, researchers develop systems that leverage deep neural networks or other techniques to generate 3D building models. This includes using AI for tasks such as semantic segmentation of building elements, style transfer for architectural design, or even the autonomous generation of entire cityscapes. Style Transfer: this applies AI techniques to transfer architectural styles from existing buildings or to design concepts to generate new building designs with similar aesthetics. Evolutionary Algorithms: this uses genetic algorithms or other evolutionary techniques to evolve and optimize building designs based on specified criteria such as energy efficiency or visual appeal. Reinforcement Learning: this trains AI agents to autonomously generate and optimize building designs through trial and error, with feedback based on predefined objectives or constraints. These are just a few broad areas where research is ongoing. Within each area, there are numerous specific methodologies and approaches being explored to improve the efficiency and realism of 3D building modeling using procedural techniques, machine learning, and AI [44; 45; 46; 47; 48]. Although there are many advantages to using procedural techniques, machine learning, and artificial intelligence to automate the process of obtaining 3D building models, there are also certain drawbacks and challenges that researchers need to consider:  
* Lack of Precision: automated processes may result in less precise or realistic models, especially when it comes to detailed architectural elements or complex textures. * Limited Generalization: models trained on one dataset may not generalize well to other cities or architectural styles, limiting the usability of these methods across different areas. * Dependency on Input Data Quality: the quality of the resulting models often depends on the quality of input data, including image resolution or accuracy of geolocation information.