MMCN Framework for AI-Driven Urban Layout Forecasting (IMAGE)
Caption
Overview of the Memory-aware Multi-Conditional generation Network (MMCN) framework for forecasting future urban layouts. The system integrates multiple modules, including a spatial memory module that captures contextual information from neighboring regions, a multi-prompt fusion module that combines urban condition inputs such as building density, building height, and road networks, and a multi-conditional control module that guides a diffusion-based generative model. Together, these components enable the model to generate spatially coherent urban layout predictions while maintaining continuity across adjacent areas.
Credit
Associate Professor Haoran Xie from JAIST
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