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

Hardik Arora, Christoph Langenhan, Frank Petzold, Viktor Eisenstadt, Klaus-Dieter Althoff

In: Proceedings of the European Conference on Product and Process Modeling 2020-2021. European Conference on Product and Process Modeling (ECPPM-2021) May 5-7 Moscow Russia European Association of Product and Process Modelling 2021.


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 publicly. 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.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence