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This research paper describes the process and results of a project to automatically classify historical buildings using aerial photography and satellite imagery. New computational scientific methods and the availability of satellite images have created more opportunities to work on automated recognition of pieces of historical architecture. In this regard, the convolutional neural network (CNN) is the main classification approach within the project. As a result, the trained model is tested using a validation data set and has a roughly 98% accuracy. In addition, being affected by urbanization and other factors, local architectural heritage faces the challenge of introducing innovations for sustainable development, with originality and authenticity being preserved in redesign and planning. Thus, this study uses a visualized quantitative analysis to analyze the research trends in Russian vernacular architecture and study new ways of coexistence between vernacular architecture, object perception and cultural ecology. The most important task of this study is to analyze the theory of coordination between the emotion social and cultural structure and the cultural ecosystem in vernacular architecture. The main contribution of this study is the proposed concept of a subjective-cultural eco-design system for vernacular architecture sustainable development to establish a 3D structural analysis design paradigm and evaluation analysis matrix, and to ensure that vernacular architecture demonstrates the ability to self-renew by continuous exchange and revision in the dynamic cycle of the current design system.
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[3] Parvin A. Architecture (and the Other 99%): Open‐Source Architecture and Design Commons. Architectural Design. 2013. 83 (6). P. 90 – 95. DOI: 10.1002/ad.1680
[4] Ingarden K. From Architecture Without Architects to Architecture After Architects. Technical Transactions. 2019. 8. P 19 – 29. DOI: 10.4467/2353737XCT.19.078.10857
[5] Kaminer T. The Efficacy of Architecture: Political Contestation and Agency. London: Routledge, 2017. DOI:10.4324/9781315693750
[6] Rudofsky B. Architecture Without Architects: A Short Introduction to Non-Pedigreed Architecture. Albuquerque, 1987.
[7] Dolgova E.D. Aesthetics of anonymous architecture. Bulletin of SGASU. Urban planning and architecture. 2016. 2 (23). P. 91 – 95. DOI: 10.17673/Vestnik.2016.02.17
[8] Gómez Martínez V., Ma D., Pellegrini P., Reimão Costa M. Vernacular Architecture and Cultural Identity in Shrinking Rural Settlements. ACE: Architecture, City and Environment. 2022. Vol. 17(50). 11389. DOI: 10.5821/ace.17.50.11389
[9] Venturi R, Brown S.D, Eisenour S. Las Vegas Lessons. Moscow: Strelka Press. 2015.
[10] Skrede J., Andersen B. Visualising the past for the future: a social semiotic reading of urban heritage, Social Semiotics. 2022. DOI: 10.1080/10350330.2022.2035654.
[11] Rozin V.M. The idea of building a new humanitarian discipline ‒ "narrative semiotics". Culture and Art. 2022. 4. P. 78 – 93. DOI: 10.7256/2454-0625.2022.4.37955
[12] Roper R. Private Visions: Outsider Art on Utah’s Cultural Landscape. Utah Historical Quarterly 1 January 2020. 88 (1). P. 58 – 76. DOI: https://doi.org/10.5406/utahhistquar.88.1.0058
[13] Mekonnen H., Bires Z., Berhanu, K. Practices and challenges of cultural heritage conservation in historical and religious heritage sites: evidence from North Shoa Zone, Amhara Region, Ethiopia. Herit Sci. 2022. 10. 172. DOI: 10.1186/s40494-022-00802-6
[14] Volodina A.V. Vernakular in culture: a shared local experience language. Current problems of theory and history of art. 2022. 12. P. 642 – 653. DOI: 10.18688/aa2212-07-51
[15] Soori M., Arezoo B., Dastres R. Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics. 2023. 3. P. 54 – 70. DOI: 10.1016/j.cogr.2023.04.001
[16] He K., Zhang X., Ren S., Sun J. Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV. USA, 27-30 June 2016. P. 770 – 778. DOI:10.1109/cvpr.2016.9
[17] Kiranyaz, S.; Avci, O.; Abdeljaber, O.; Ince, T.; Gabbouj, M.; Inman, D.J. 1D convolutional neural networks and applications: A survey. Mech. Syst. Signal Process. 2021. 151. 107398. DOI: 10.1016/j.ymssp.2020.107398.
[18] Singh P., Manure A. Introduction to TensorFlow 2.0. In Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python. Apress: Berkeley, CA, USA, 2020. P. 1 – 24. DOI: 10.1007/978-1-4842-5558-2_1
[19] Moscoso-García P., Quesada-Molina F. Analysis of Passive Strategies in Traditional Vernacular Architecture. Buildings. 2023. 13 (8). 1984. DOI: 10.3390/buildings13081984
[20] Savaşkan M.O., Özener O.Ö. H-BIM applications for vernacular architecture: a historic rural house case study in Bursa, Turkey". Engineering, Construction and Architectural Management. 2022. Vol. ahead-of-print No. ahead-of-print. DOI: 10.1108/ECAM-01-2022-0017
Krestinenko N.V. Vernacular architecture in the space of a modern city, based on deep learning methods and three-dimensional structural analysis. Construction Materials and Products. 2023. 6 (6). 9. https://doi.org/10.58224/2618-7183-2023-6-6-9