Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs

In this paper, a data-driven generative method is applied to generate synthetic space allocation probability layout. This generated layout could be helpful in the early stage of an architectural design. For this task, a specific training dataset is generated which is used to train the cGAN model. The training dataset consists of 300 existing apartment layouts which are coloured in a set of low feature representation. The cGAN model is trained with this dataset and the trained model is evaluated based on the quality of its generated layouts regarding the five pre-defined topological and geometrical benchmarks.