The study of high-performance generation methods for rural plan based on generative adversarial network

Abstract

In China, traditional village layouts are dynamic, harmoniously integrated with the natural environment, and rich in unique cultural characteristics. However, rapidly constructed villages often lack professional design, resulting in overly simple layouts and causing the villages to lose their traditional characteristics. Artificial intelligence holds the potential to alleviate this specific challenge. This study employs CGAN to generate comprehensive village layouts based on archetypal traditional villages, while also exploring parameters and network architectures to enhance result quality. The research address on traditional villages in southwestern Hubei, refining generative factors, introducing image-based geographic scales, and employing machine vision to address data scarcity. The key findings of this study includes: 1) The research explores a class of AI-generated evaluation metrics suitable for village layout generation. 2) It confirms that the combination of the Unet_256 generator with the LSGAN architecture yields the best results in image generation. 3) It is observed that the optimal generation results are achieved when the equivalent geographic scale of the image is 150 m × 150 m. The study validates that GANs can be effectively applied in the village layout, producing layout results that incorporate traditional local experiences. This provides a novel approach to village layout.

Keywords

Generative adversarial network / Traditional village layout structure design / Generate result optimization / Deep learning / Space planning problem

References

[1] Baduge, S.K. , Thilakarathna, S. , Perera, J.S. , Arashpour, M. , Sharafi, P. , Teodosio, B. , Shringi, A. , Mendis, P. , 2022. Artificial intelligence and smart vision for building and construction 4.0: machine and deep learning methods and applications. Autom. ConStruct. 141.

[2] Creswell, A. , White, T. , Dumoulin, V. , Arulkumaran, K. , Sengupta, B. , Bharath, A.A. , 2018. Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35, 53- 65.

[3] Feng, J.C. , 2013. Dilemma and solution of traditional villages: a discussion on traditional villages as another type of cultural heritage. Folk cult. Forum 7-12 (in Chinese).

[4] Gan, V.J.L. , 2022. BIM-based graph data model for automatic generative design of modular buildings. Autom. ConStruct. 134.

[5] Goodfellow, I. , Pouget-Abadie, J. , Mirza, M. , Xu, B. , WardeFarley, D. , Ozair, S. , Courville, A. , Bengio, Y. , 2014. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27.

[6] Gulrajani, I. , Ahmed, F. , Arjovsky, M. , Dumoulin, V. , Courville, A.C. , 2017. Improved training of wasserstein gans. Adv. Neural Inf. Process. Syst. 30.

[7] Hu, B. Bin , 2017. Blue Book of Chinese Traditional Villages: Investigation Report on the Protection of Chinese Traditional Villages. Social Sciences Academic Press, Beijing, China (in Chinese).

[8] Hu, P. , 2008. Analysis of Factors Influencing Traditional Residence Settlement in Western Hubei. Huazhong Agricultural University(in Chinese).

[9] Huang, W. , Zheng, H. , 2018. Architectural drawings recognition and generation through machine learning. In: Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture, Mexico City, Mexico, pp. 18-20.

[10] Huang, X.J. , 2020. Digitized Preservation and Inheritance of Traditional Cultural Heritage in Ancient Villages. People's Trib, pp. 140-141 (in Chinese).

[11] Isola, P. , Zhu, J.-Y. , Zhou, T. , Efros, A.A. , 2017. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125-1134.

[12] Jiang, G. , 2013. Research on the Conservation Planning of Traditional Village. Central South University (in Chinese).

[13] Jiang, Y. , Li, N. , Wang, Z. , 2023. Parametric reconstruction of traditional village morphology based on the space gene perspective—the case study of xiaoxi village in western hunan, China. Sustainability 15, 2088.

[14] Jin, P.P. , 2021. Research on application of style transfer and conditional generative adversarial networks. In: Urban Fabric Generation. Nanjing University (in Chinese).

[15] Lai, Y.T. , 2021. Research on Automatic Generation of Building Layout Based on Pix2pix. South China University of Technology(in Chinese).

[16] LeCun, Y. , Boser, B. , Denker, J.S. , Henderson, D. , Howard, R.E. , Hubbard, W. , Jackel, L.D. , 1989. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541- 551.

[17] Li, B.H. , Liu, P.L. , Dou, Y. Di , Zeng, C. , Chen, C. , 2017a. Transformation and development of human settlement environment in Chinese traditional villages and research progress. Geogr. Res. 36, 1886-1900 (in Chinese).

[18] Li, B.H. , Luo, Q. , Liu, P.L. , Zhang, J.Q. , 2017b. Knowledge maps analysis of traditional villages research in ChinaBased on the citespace method. Econ. Geogr. 37, 207-214+232 (in Chinese).

[19] Li, X. , 2019. Using Artificial Intelligence to Simulate the Growth of Traditional Rural Settlement. Tianjin University (in Chinese).

[20] Li, X.F. , 2009. Two Lakes Dwellings. China Architecture & Building Press, Beijing: China (in Chinese).

[21] Lin, W.Q. , 2020. Research on Automatic Generation of Primary School Schoolyard Layout Based on Deep Learning. South China University of Technology (in Chinese).

[22] Liu, X.H. , Wang, Y.P. , Liu, H. , 2022. Research on 'one button' artificial intelligence design system for rural residences. Archicreation 163-169 (in Chinese).

[23] Liu, X.Q. , Wang, S.M. , 2015. Difficulties and countermeasures of Chinese traditional villages conservation. Agric. Hist. China 34, 99-110 (in Chinese).

[24] Liu, Y. , Fang, C. , Yang, Z. , Wang, X. , Zhou, Z. , Deng, Q. , Liang, L. , 2022. Exploration on machine learning layout generation of Chinese private garden in Southern Yangtze. In: Proceedings of the 2021 DigitalFUTURES: the 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021). Springer, Singapore, pp. 35-44.

[25] Liu, Z. , 2021. Research on Generating Plan of Rural Residence Based on Python Programming Language. Huazhong University of Science and Technology (in Chinese).

[26] Liu, Z. , Wang, Y.P. , Liu, X.H. , 2022. Research on generating plan of rural residential based on GH_Python: taking southwestern Hubei as an example. In: Proceedings of the National Symposium on Architectural Digital Technology Teaching and Research 2022. Xiamen,Fujian,China, p. 6 (in Chinese).

[27] Lu, X.Y. , 2022. Research on Traditional Village Layout Generation Based on Algorithmic Language. Huazhong University of Science and Technology (in Chinese).

[28] Luo, Z. , Huang, W. , 2022. FloorplanGAN: vector residential floorplan adversarial generation. Autom. ConStruct. 142.

[29] Mao, X. , Li, Q. , Xie, H. , Lau, R.Y.K. , Wang, Z. , Paul Smolley, S. , 2017. Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794-2802.

[30] McCulloch, W.S. , Pitts, W. , 1943. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115- 133.

[31] Meng, Y. , Dai, S.Z. , Wen, X.F. , 2015. The problems and measures of rural planning. Planners 31, 143-147 (in Chinese).

[32] Mirza, M. , 2014. Conditional generative adversarial nets[J]. arXiv preprint arXiv: 1411.1784.

[33] Pizarro, P.N. , Hitschfeld, N. , Sipiran, I. , Saavedra, J.M.J. A. in C. , 2022. Automatic Floor Plan Analysis and Recognition, vol. 140, 104348.

[34] Rahbar, M. , Mahdavinejad, M. , Markazi, A.H.D. , Bemanian, M. , 2022. Architectural layout design through deep learning and agent-based modeling: a hybrid approach. J. Build. Eng. 47.

[35] Redmon, J. , Divvala, S. , Girshick, R. , Farhadi, A. , 2016. You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788.

[36] Rumelhart, D.E. , Hinton, G.E. , Williams, R.J. , 1986. Learning representations by back-propagating errors. Nature 323, 533- 536.

[37] Salimans, T. , Goodfellow, I. , Zaremba, W. , Cheung, V. , Radford, A. , Chen, X. , 2016. Improved techniques for training gans. Adv. Neural Inf. Process. Syst. 29.

[38] Song, H.Y. , 2024. Improving the level of rural construction: realistic foundation, key issues, and countermeasures. World Agric. 5-16 (in Chinese).

[39] Sun, Y. , Zhang, S.W. , 2017. A review of rural planning research and future research prospect. Urban Plan. Forum 74-80 (in Chinese).

[40] Wan, D. , Zhao, R. , Zhang, S. , Liu, H. , Guo, L. , Li, P. , Ding, L. , 2023. A deep learning-based approach to generating comprehensive building façades for low-rise housing. Sustainability 15.

[41] Wang, L. , 1999. Conservation and renewal of traditional rural buildings: insights from German village renewal planning. Architects' J. 16-21 (in Chinese).

[42] Wang, X.-Y. , Yang, Y. , Zhang, K. , 2018. Customization and generation of floor plans based on graph transformations. Autom. ConStruct. 94, 405- 416.

[43] Wang, Y.F. , Yuan, Q. , 2021. Inheritance and optimization of rural space feature based on the application of morphological gene bank: the case of rural settlements in heilongjiang Province. Planners 37, 84-92 (in Chinese).

[44] Weber, R.E. , Mueller, C. , Reinhart, C. , 2022. Automated floorplan generation in architectural design: a review of methods and applications. Autom. ConStruct. 140

[45] Wu, A.N. , Stouffs, R. , Biljecki, F. , 2022. Generative Adversarial Networks in the built environment: a comprehensive review of the application of GANs across data types and scales. Build. Environ. 223.

[46] Wu, M. , 2011. Study on the Strategy of Sustainable Development of Village Landscape in Southwest Hubei. Huazhong Agricultural University (in Chinese).

[47] Xia, X.T. , 2021. Study on the Protection and Development of Traditional Villages in Western Hubei. Huazhong University of Science and Technology (in Chinese).

[48] Xiao, H. , 2018. Research on Traditional Settlement Pattern in the Southwest of Hubei Province. Hubei University of Technology (in Chinese).

[49] Xu, M.Z. , 2021. Parametric Generation of Main Structure of Stilted Building. Huazhong University of Science and Technology (in Chinese).

[50] Yang, X.J. , 2023. Research on the Activation of Public Space of Traditional Villages in Xiyan Town under the Centralized Continuous Protection and Development. Xi'an University of Architecture and Technology (in Chinese).

[51] Yin, C.Y. , Sun, W.B. , 2022. Research on the revitalization design strategy of traditional villages from the perspective of aesthetics. Hubei Inst. Fine Arts J. 107-111 (in Chinese).

[52] Zang, X. , Wang, Q. , 2019. The evolution of the urban resilience concept, and its research contents and development trend. Sci. Technol. Rev. 37, 94- 104.

[53] Zhang, L.G. , 2001. Wuling Tujia. SDX Joint Publishing Company, Shanghai, China (in Chinese).

[54] Zhang, L.G. , 1995. Stilted buildings: old houses of Tujia people. Chinese Art Dig 28-31 (in Chinese).

[55] Zhang, R.J. , Gao, Y. , Li, J.Q. , 2021. The research hotspots, progress and prospects of traditional Chinese villages in the past 30 years. Mod. Urban Res. 1-7+15 (in Chinese).

[56] Zheng, H. , Keyao, A.N. , Jingxuan, W.E.I. , Yue, R.E.N. , 2020. Apartment floor plans generation via generative adversarial networks. In: 25th International Conference of the Association for Computer-Aided Architectural Design Research in Asia(CAADRIA 2020): RE: Anthropocene, Design in the Age of Humans. The Association for Computer-Aided Architectural Design Research in Asia, pp. 601-610.

[57] Zheng, L. , Yang, C. , Wang, P. , 2022. Value analysis and construction path of ethnic villages under the perspective of rural revitalization——based on the investigation of ethnic areas in southwest Hubei. J. Ethnol. 13, 57-68+148 (in Chinese).

[58] Zheng, Q. , 2023. The Characteristics and Optimization Path of the Spatial Organization of Rural Contiguous Development in Eastern Hubei Mountainous Area. Huazhong University of Science and Technology (in Chinese).

RIGHTS & PERMISSIONS

The Author(s). Publishing services by Elsevier B.V. on behalf of Higher Education Press and KeAi.