Analysis of the new architectural dataset NeoFaçade and its potential in machine learning

• Autorzy: Kowalska Bianka , Baran Hubert , Hardzetski Daniil , Kwaśnicka Halina , Marcinów Aleksandra , Biegańska Małgorzata

Identyfikatory

DOI

10.37190/arc240408

Warianty tytułu

PL

Analiza nowego zbioru danych architektonicznych NeoFaçade oraz jego potencjału w uczeniu maszynowym

• Języki publikacji: EN

Abstrakty

EN

The presence of artificial intelligence (AI) in architecture has been growing rapidly in recent years. The collaboration between architects and AI developers has led to significant improvements in various design applications. Further development of machine learning techniques is highly dependent on the availability of large, structured datasets. The aim of the article is to demonstrate the potential of a novel dataset, NeoFaçade, which contains annotated pictures of historical tenements. A comparison of the dataset with existing benchmark datasets, the CMP Façade and the Paris Art- Deco datasets, highlights its exceptional features. Its applications in three machine learning tasks are also presented: semantic segmentation, image translation and image generation. In all three tasks, the models trained with NeoFaçade provide satisfactory results and indicate the great potential of this collection. The planned further development of the dataset will allow the training of more precise models that will be able to distinguish more elements and features of the façades and assist architects in designing tenements. Key words: dataset, image processing, machine learning, architecture

PL

Rola sztucznej inteligencji (AI) w architekturze gwałtownie wzrosła w ciągu ostatnich lat. Współpraca między architektami i programistami AI doprowadziła do usprawnień w wielu dziedzinach projektowych. Dalszy rozwój technik maszynowego uczenia w znacznym stopniu zależy od dostępności dużych i ustrukturyzowanych zbiorów danych. Celem autorów artykułu jest pokazanie potencjału nowego zbioru danych, nazwanego NeoFaçade, zawierającego opisane (anotowane) obrazy kamienic historycznych. Porównując zbiór z innymi ogólnodostępnymi zbiorami – CMP Facade oraz Paris ArtDeco – podkreślono jego potencjalną użyteczność. Zaprezentowane również zostało wykorzystanie zbioru w trzech zadaniach uczenia maszynowego: segmentacji semantycznej, translacji obrazów z generacji obrazów. We wszystkich trzech zadaniach modele wytrenowane na zbiorze NeoFaçade dają satysfakcjonujące wyniki i wskazują na wysoki potencjał zbioru. Planowany dalszy rozwój zbioru umożliwi wytrenowanie dokładniejszych modeli, które będą w stanie rozróżniać więcej elementów i cech fasad oraz wspomagać architektów w projektowaniu kamienic.

Słowa kluczowe

EN

dataset; image processing; machine learning; architecture

PL

zbiór danych; przetwarzanie obrazów; uczenie maszynowe; architektura

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Architectus
• Rocznik: 2024
• Tom: Nr 4 (80)
• Strony: 75--84
• Opis fizyczny: Bibliogr. 22 poz., ryus., tab.

Twórcy

autor

Kowalska Bianka

• Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland

• https://orcid.org/0009-0008-6289-0762

autor

Baran Hubert

• Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland

• https://orcid.org/0009-0001-2976-4923

autor

Hardzetski Daniil

• Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland

• https://orcid.org/0009-0001-2815-8948

autor

Kwaśnicka Halina

• Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland

• https://orcid.org/0000-0002-4368-4110

autor

Marcinów Aleksandra

• Faculty of Architecture, Wrocław University of Science and Technology, Poland

• https://orcid.org/0000-0003-4409-3563

autor

Biegańska Małgorzata

• Faculty of Architecture, Wrocław University of Science and Technology, Poland

• https://orcid.org/0009-0005-4302-2618

Bibliografia

• Bölek, Buse, Osman Tutal, and Hakan Özbaşaran. “A systematic review on artificial intelligence applications in architecture.” Journal of Design for Resilience in Architecture and Planning 4, no 1 (2023): 91–104. https://doi.org/10.47818/DRArch.2023.v4i1085.
• Enjellina, Eleonora Vilgia Putri Beyan, and Anastasya Gisela Cinintya Rossy. “Review of AI Image Generator: Influences, challenges, and future prospects for architectural field.” Journal of Artificial Intelligence in Architecture (JARINA) 2, no. 1 (2023): 53–65. https://doi.org/10.24002/jarina.v2i1.6662.
• Gadde, Raghudeep, Renaud Marlet, and Nikos Paragios. “Learning grammars for architecture-specific facade parsing.” International Journal of Computer Vision 117, no. 3 (2016): 290–316. https://doi.org/10.1007/s11263-016-0887-4.
• Gui, Yingbin, Biao Zhou, Xiongyao Xie, Wensheng Li, and Xifang Zhou. “GAN-Based Method for generative design of visual comfort in underground space.” IOP Conference Series: Earth and Environmental Science 861, no. 7 (2021): 072015. https://doi.org/10.1088/1755-1315/861/7/072015.
• Isola, Phillip, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. “Imageto- Image Translation with conditional adversarial networks.” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017): 5967–76. https://doi.org/10.1109/CVPR.2017.632.
• Li, Chengyuan, Tianyu Zhang, Xusheng Du, Ye Zhang, and Haoran Xie. “Generative AI models for different steps in architectural design: A literature review.” Frontiers of Architectural Research (2024). https://doi.org/10.1016/j.foar.2024.10.001.
• Liang, Ci-Jyun, Thai-Hoa Le, Youngjib Ham, Bharadwaj R.K. Mantha, Marvin H. Cheng, and Jacob J. Lin. “Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry.” Automation in Construction 162 (June 2024): 105369. https://doi.org/10.1016/j.autcon.2024.105369.
• Marcinów, Aleksandra, Małgorzata Biegańska, Bianka Kowalska, Hubert Baran, Daniil Hardzetski, and Halina Kwaśnicka. “Building a dataset of Wrocław’s historic tenements: Image annotation for machine learning applications.” Architectus 79, no. 3 (2024): 55–64. https://doi.org/10.37190/arc240306.
• Martinovic, Andelo, and Luc Van Gool. “Bayesian grammar learning for inverse procedural modeling.” 2013 IEEE Conference on Computer Vision and Pattern Recognition (2013a): 201–8. https://doi.org/10.1109/CVPR.2013.33.
• Martinovic, Andelo, and Luc Van Gool. “Early Parsing for 2D Stochastic Context Free Grammars.” Technical Report KUL/ESAT/PSI/1301, KU Leuven, 2013b.
• Nabizadeh Rafsanjani, Hamed, and Amir Hossein Nabizadeh. “Towards human-centered artificial intelligence (AI) in architecture, engineering, and construction (AEC) industry.” Computers in Human Behavior Reports 11 (August 2023): 100319. https://doi.org/10.1016/j. chbr.2023.100319. Newton, David. “Generative deep learning in architectural design.” Technology| Architecture + Design 3, no. 2 (2019): 176–89. https://doi.org/10.1080/24751448.2019.1640536.
• Pizarro, Pablo N., Leonardo M. Massone, Fabián R. Rojas, and Rafael O. Ruiz. “Use of convolutional networks in the conceptual structural design of shear wall buildings layout.” Engineering Structures 239 (2021): 112311. https://doi.org/10.1016/j.engstruct.2021.112311.
• Peng, Jizong, Guillermo Estrada, Marco Pedersoli, and Christian Desrosiers. “Deep co-training for semi-supervised image segmentation.” Pattern Recognition 107 (November 2020): 107269. https://doi.org/10.1016/j.patcog.2020.107269.
• Ploennigs, Joern, and Markus Berger. “AI art in architecture.” AI in Civil Engineering 2, 8 (2023). https://doi.org/10.1007/s43503-023-00018-y.
• Rhee, Jinmo, and Jae-Won Chung. “Applicability of Artificial Intelligence in Apartment Complex Design.” In Annual Conference in Architectural Institute of Korea, 2019. Riemenschneider, Hayko, Urlich Krispel, Wolfgang Thaller, Michael Donoser,
• Sven Havemann, Dieter Fellner, and Horst Bischof. “Irregular lattices for complex shape grammar facade parsing.” 2012 IEEE Conference on Computer Vision and Pattern Recognition (2012): 1640–47. https://doi.org/10.1109/CVPR.2012.6247857.
• Sourek, Michal. “AI in architecture and engineering from misconceptions to game-changing prospects.” Architectural Intelligence 3, 4 (2024). https://doi.org/10.1007/s44223-023-00046-9.
• Tyleček, Radim, and Radim Šára. “Spatial pattern templates for recognition of objects with regular structure.” In Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, edited by Joachim Weickert, Matthias Hein, Bernt Schiele, Springer, 2013. https://doi.org/10.1007/978-3-642-40602-7_39.
• Xie, Enze, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, and Ping Luo. “SegFormer: Simple and efficient design for semantic segmentation with transformers.” (2021). https://doi.org/10.48550/arXiv.2105.15203.
• Yau, Ho Man, Theodoros Dounas, Wassim Jabi, and Davide Lombardi. “Timber joints analysis and design using shape and graph grammarbased machine learning approach.” In Digital Design Reconsidered– Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023), 20–23 September 2023, Graz, Austria, edited by Wolfgang Dokonal,
• Urs Hirschberg, Gabriel Wurzer. Vol. 1. eCAADe, 2023. https://doi.org/ 10.52842/conf.ecaade.2023.1.569.
• Yin, Wei, Yifan Liu, Chunhua Shen, and Baichuan Sun. “SSIW.” Accessed 2023. https://github.com/Xpitfire/SSIW/.