METIS-GAN: An approach to generate spatial configurations using deep learning and semantic building models

In order to recommend architects design options, a system was developed which uses artificial intelligence (AI) methods of case-based reasoning (CBR) and deep learning. Since the system uses deep learning, it requires a sufficient amount of data for training, but currently, not enough amount of semantic building data is available publically. In this paper, a Generative Adversarial Network (GAN) is considered to generate the semantic building data to train a Deep Neural Network (DNN) to recommend design options.