# Geovisualization of Buildings: AI vs. Procedural Modeling
## 4 Challenges, Limitations, and Future Directions  
Despite the significant advancements offered by the proposed AI-driven procedural modeling approach in enhancing geovisualization and automating 3D building modeling, there are several challenges and limitations that need to be addressed to fully realize its potential in practical applications. One of the primary challenges lies in the quality and availability of data. The accuracy and effectiveness of the AI models are closely tied to the quality and diversity of the input datasets. For models based on verbal descriptions, inconsistencies or incomplete data can result in errors during the generation of 3D models. Similarly, the optical recognition approach is highly dependent on the quality of images or videos; poor resolution, occlusions, or unfavorable lighting conditions can hinder accurate feature extraction and lead to less reliable outputs. Another notable limitation is the model's ability to generalize across different architectural styles and environments. Models that are trained on specific datasets may encounter difficulties when applied to new or unseen types of buildings, particularly those with unique or complex architectural features. This limitation can restrict the method's applicability in diverse or global contexts, where architectural variability is more pronounced. Additionally, the computational demands associated with training and deploying deep learning models, particularly for optical recognition, are substantial. High-performance computing resources are often required, which may not be readily available in all practical settings, especially in resource-constrained environments. This limitation could hinder the widespread adoption of these methods. A further challenge is the issue of interpretability and transparency within the AI models. Although these models can generate accurate 3D representations, the underlying decision-making processes often lack transparency, making it difficult to understand how certain architectural features were interpreted or why specific design decisions were made. This can be a significant drawback in fields that require a high degree of interpretability, such as urban planning or heritage conservation. Moreover, integrating AI-driven procedural modeling into existing architectural and urban planning workflows may present practical difficulties. Compatibility with existing software and tools, as well as the need for specialized knowledge to operate these AI systems, could pose significant barriers to adoption. Looking towards the future, several directions can be explored to address these challenges and enhance the proposed methodology. First, there is a need for richer and more diverse datasets that encompass a broader range of architectural styles and environmental contexts, which would help improve the models' ability to generalize. Additionally, combining verbal and visual inputs into a hybrid model could leverage the strengths of both modalities, thereby overcoming some of the limitations associated with each approach. Advances in AI techniques, such as the use of Generative Adversarial Networks (GANs) for data augmentation and reinforcement learning for model optimization, could further enhance the quality and adaptability of the models. These advanced techniques might also contribute to generating more realistic and context-aware 3D models. Improving the transparency and interpretability of AI models should also be a priority. The development of explainable AI (XAI) techniques could make the decision-making processes within these models more understandable, thus increasing trust and usability in professional applications. Additionally, research into more resource-efficient AI models that can operate effectively on lower-end hardware or within cloud-based systems could make these tools more accessible and scalable. Finally, future work should focus on improving the integration of AI-driven procedural modeling tools with industry-standard software and workflows.