Despite its advancements, ControlCity may face several limitations and potential failure cases:
Data Quality and Completeness: The model's performance heavily relies on the quality and completeness of the input data from OSM and Wikipedia. In regions with sparse or inaccurate data, the generated building footprints may be suboptimal. To address this, a data augmentation strategy could be employed, utilizing synthetic data generation techniques to fill gaps in the training dataset.
Generalization to Diverse Urban Morphologies: While ControlCity demonstrates strong generalization capabilities, it may struggle with cities that have unique or unconventional urban layouts. To mitigate this, the model could be refined by incorporating a more diverse training dataset that includes a wider variety of urban forms, or by employing a hierarchical architecture that allows for the specialization of different urban morphology types.
Computational Efficiency: The multimodal diffusion model may require significant computational resources, particularly when processing large datasets. Implementing model pruning techniques or optimizing the architecture for efficiency could help reduce the computational burden while maintaining performance.
Interpretability: The complexity of the model may hinder interpretability, making it difficult for urban planners to understand the rationale behind certain generated outputs. Incorporating explainable AI techniques could enhance transparency, allowing users to gain insights into how specific inputs influence the generated building footprints.
By addressing these limitations through targeted refinements and alternative architectural strategies, ControlCity can improve its robustness and applicability across various urban contexts.