# Geovisualization of Buildings: AI vs. Procedural Modeling
## 5 Conclusions  
The comparative analysis of AI-based procedural modeling using verbal descriptions versus optical recognition of building images provides valuable insights into the strengths and limitations of each input modality. By assessing metrics such as model accuracy, fidelity to real-world structures, computational efficiency, and user satisfaction, the study underscores the importance of input modality selection in the design and implementation of modeling systems. The implications for practitioners and researchers are profound, as the choice of input modality can significantly impact the efficacy and suitability of the generated models. Verbal descriptions offer a straightforward method for capturing detailed specifications, especially when textual information is readily available. Natural language processing (NLP) techniques can efficiently parse and extract relevant data from these descriptions, facilitating accurate model generation. However, the fidelity of models derived from verbal descriptions heavily depends on the completeness and clarity of the provided information. This approach may fall short in scenarios where intricate visual details are critical, as it might not capture the nuanced architectural features that are easily perceivable in images. On the other hand, the optical recognition of building images leverages the power of computer vision algorithms, particularly convolutional neural networks (CNNs), to extract detailed architectural features from visual data. This method excels in capturing the contextual richness and fine-grained details of building structures. By identifying and classifying elements such as facades, windows, and doors through image segmentation and feature detection, this approach can produce highly accurate and faithful models. However, it requires substantial computational resources and high-quality, annotated datasets to train the algorithms effectively. Hybrid approaches that combine verbal descriptions with optical recognition hold promise for maximizing the strengths and mitigating the weaknesses of each modality. By integrating textual and visual data, these methods can achieve a higher level of detail and accuracy in model generation. For instance, while textual descriptions can provide a broad framework and specific dimensions, visual data can enrich the model with precise architectural features and contextual information. This synergy can enhance the overall fidelity and usability of the generated models. Scalability and adaptability are critical considerations for the practical application of procedural modeling systems. Cloud-based infrastructure and distributed computing technologies enable the efficient handling of large-scale modeling tasks. Modular software architectures and open-source frameworks foster interoperability and extensibility, allowing developers to integrate custom modules and adapt systems to specific domain contexts. This flexibility ensures that procedural modeling can accommodate diverse project requirements and scales, from individual buildings to extensive urban planning projects. Conclusions from this research and the findings of this analysis emphasize the importance of selecting the appropriate input modality based on the specific requirements, constraints, and objectives of the modeling task. For example, in scenarios where detailed visual specifications are readily available in textual form, such as architectural blueprints or schematic diagrams, verbal descriptions may suffice. Conversely, in environments rich with visual data, such as photorealistic images or video streams, optical recognition may offer superior results. Ultimately, the choice of input modality should align with the desired outcomes of the modeling process, whether it be accuracy, efficiency, or user satisfaction. By carefully considering the strengths and limitations of each approach, practitioners can make informed decisions that enhance the effectiveness of AI-based procedural modeling.