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Deep Learning Architect: Classification for Architectural Design Through the Eye of Artificial Intelligence

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Computational Urban Planning and Management for Smart Cities (CUPUM 2019)

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))


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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  • Abdel-Hamid O, Mohamed AR, Jiang H, Penn G (2012) Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP), Kyoto international conference center, Kyoto, 25–30 March 2012

    Google Scholar 

  • Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. ArXiv Preprint ArXiv:1409.0473

  • Cai BY, Li X, Seiferling I, Ratti C (2018) Treepedia 2.0: applying deep learning for large-scale quantification of urban tree cover. In: 2018 IEEE international congress on big data (BigData Congress), Seattle, 25–30 June 2018

    Google Scholar 

  • Doersch C, Singh S, Gupta A, Sivic J Efros AA (2012) What makes paris look like paris? In: ACM transactions on graphics (SIGGRAPH 2012), vol 31(4). ACM Press, New York

    Article  Google Scholar 

  • Elgammal A, Mazzone M, Liu B, Kim D (2018) The shape of art history in the eyes of the machine. ArXiv Preprint, ArXiv:1801.07729

  • Forty A (2000) Words and buildings: a vocabulary of modern architecture. Thames & Hudson, New York

    Google Scholar 

  • Frampton K (1992) Modern architecture: A critical history (3rd edn, Revised and Enlarged). London: Thames and Hudson

    Google Scholar 

  • Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, 26 June to 1 July 2016

    Google Scholar 

  • Girshick R, Donahue J, Darrell T, Berkeley UC Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE conference on computer vision and pattern recognition, Columbus, 24–27 June 2014

    Google Scholar 

  • He K, Zhang X, Ren S, Jian S (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, 26 June to 1 July 2016

    Google Scholar 

  • Hitchcock HR, Johnson P (1932) The international style: architecture since 1922. W.W. Norton & Company, New York

    Google Scholar 

  • Johnson P, Wigley M (1988) Deconstructivist architecture: the Museum of Modern Art. Museum of Modern Art, New York

    Google Scholar 

  • Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer-Verlag, New York

    Google Scholar 

  • Kant I (1952) The critique of judgment (1790). (trans: Meredith JC). Clarendon Press, Oxford

    Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Advances In Neural Information Processing Systems, pp. 1097–1105

    Google Scholar 

  • Kron J, Slesin S (1984) High-tech: the industrial style and source book for the home. Clarkson Potter, New York

    Google Scholar 

  • Lee S, Maisonneuve N, Crandall D, Efros AA Sivic J (2015) Linking past to present: discovering style in two centuries of architecture. In: 2015 IEEE international conference on computational photography (ICCP), Houston, 24–26 April 2015

    Google Scholar 

  • Li J, Yao L, Hendriks E, Wang JZ (2012) Rhythmic brushstrokes distinguish van gogh from his contemporaries: findings via automated brushstroke extraction. IEEE Trans Pattern Anal Mach Intell 34(6):1159–1176

    Article  Google Scholar 

  • Llamas J, Lerones PM, Medina R, Zalama E, Gómez-García-Bermejo J (2017) Classification of architectural heritage images using deep learning techniques. Appl Sci 7(10):992

    Article  Google Scholar 

  • Obeso AM, Vázquez GMS, Acosta AAR, Benois-Pineau J (2017) Connoisseur: classification of styles of Mexican architectural heritage with deep learning and visual attention prediction. In: 15th international workshop on content-based multimedia indexing (CBMI), Florence, 19–21 June 2017

    Google Scholar 

  • Onians J (1988) Bearers of meaning: the classical orders in antiquity, the middle ages, and the renaissance. Princeton University Press, Princeton

    Google Scholar 

  • Saleh B, Abe K, Arora RS, Elgammal A (2016) Toward automated discovery of artistic influence. Multimed Tools Appl 75(7):3565–3591

    Article  Google Scholar 

  • Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV), Venice, 22–29 October 2017

    Google Scholar 

  • Shalunts G, Haxhimusa Y, Sablatnig R (2011) Architectural style classification of building facade windows. In: Bebis G, Boyle R, Parvin B, Koracin D, Fowlkes C, Wang S, Choi M-H, Mantler S, Schulze J, Acevedo D, Mueller K, Michael P (eds) Advances in visual computing, ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg, New York, pp 280–289

    Chapter  Google Scholar 

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ArXiv preprint ArXiv:1409.1556

  • Stern RAM (1993) Frank O. Gehry: architecture with a serious smile. In: Futagawa Y (ed) Frank O. Gehry. GA Architect 10. A.D.A. Edita, Tokyo, pp 8–9

    Google Scholar 

  • Wolfflin H (1950) Principles of art history: the problem of the development of style in later art (1915). (7th Edition Trans. Hottinger MD) Dover, New York

    Google Scholar 

  • Zhang F, Zhou B, Liu L, Liu Y, Fung HH, Lin H, Ratti C (2018) Measuring human perceptions of a large-scale urban region using machine learning. Landsc Urban Plan 180:148–160

    Article  Google Scholar 

  • Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27 (NIPS 2014), Curran Associates, Inc, pp 487–495

    Google Scholar 

  • Zoph B, Shlens J (2018) Learning transferable architectures for scalable image recognition. ArXiv Preprint ArXiv:1707.07012

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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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Correspondence to Yuji Yoshimura .

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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.

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