Abstract
This paper applies state-of-the-art techniques in deep learning and computer vision to measure visual similarities between architectural designs by different architects. Using a dataset consisting of web-scraped images and an original collection of images of architectural works, we first train a deep convolutional neural network (DCNN) model capable of achieving 73% accuracy in classifying works belonging to 34 different architects. By examining the weights in the trained DCNN model, we are able to quantitatively measure the visual similarities between architects that are implicitly learned by our model. Using this measure, we cluster architects that are identified to be similar and compare our findings to conventional classification made by architectural historians and theorists. Our clustering of architectural designs remarkably corroborates conventional views in architectural history, and the learned architectural features also cohere with the traditional understanding of architectural designs.
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Acknowledgements
The authors thank Cisco, Teck, Dover Corporation, Lab Campus, Anas, SNCF Gares and Connexions, Brose, Allianz, UBER, Austrian Institute of Technology, Fraunhofer Institute, Kuwait-MIT Center for Natural Resources, SMART-Singapore-MIT Alliance for Research and Technology, AMS Institute, Shenzhen, Amsterdam, Victoria State Government and all the members of the MIT Senseable City Lab Consortium for supporting this research.
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Yoshimura, Y., Cai, B., Wang, Z., Ratti, C. (2019). Deep Learning Architect: Classification for Architectural Design Through the Eye of Artificial Intelligence. In: Geertman, S., Zhan, Q., Allan, A., Pettit, C. (eds) Computational Urban Planning and Management for Smart Cities. CUPUM 2019. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-19424-6_14
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