Abstract
The building stock of a city is characterized by typological and structural inhomogeneity. In the urban web coexist many types of buildings of different historical periods e.g. Byzantine (medieval) temples, neoclassical (art nouveau, art deco), apartment buildings (interwar and postwar buildings), which have been built with different materials and styles.
Nevertheless, the “type” of such buildings (e.g. period-based, neoclassical, interwar and postwar buildings) is a repetitive recognizable organizational structure, which is perceived as part of a class of repetitive objects characterized by their common morphological features. Until nowadays, the recognition of the “type” was the result of collective mental mechanisms, which society processed through everyday construction experience. Today, the type pre-exists in the mind of the culture heritage (CH) researchers (architects, archaeologists, historians of art etc.) as a result of their special education. This paper focuses on the typological approach through the classification of data using methods of the field of Artificial Intelligence (AI), and more specifically Deep Learning. It is a process that may: a) lead to the comparative evaluation of the common and repetitive elements of different projects with architectural criteria, b) enable the huge classification of data in a short period of time, c) make possible the digital management of the available CH building stock and d) export useful conclusions that will contribute to the further study of the CH data in the future.
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Notes
- 1.
International movement developed in 1890-1910 and distinguished for the sophistication of forms mainly from elements of nature.
- 2.
Artist movement in Vienna of 1897, aimed at creating a style artist who would not be influenced by any artistic period.
- 3.
Movement that appeared in Paris from 1830 to the end of the 19th century.
- 4.
Movement that appeared in Italy in 1600-1750 and is strongly associated with painting.
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The publication of this paper has been partly supported by the University of Piraeus Research Center (UPRC).
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Kosmopoulos, I., Siountri, K., Anagnostopoulos, CN. (2022). The Use of Deep Learning in the Classification of Buildings at the Post-revolutionary City of Athens. In: Moropoulou, A., Georgopoulos, A., Doulamis, A., Ioannides, M., Ronchi, A. (eds) Trandisciplinary Multispectral Modelling and Cooperation for the Preservation of Cultural Heritage. TMM_CH 2021. Communications in Computer and Information Science, vol 1574. Springer, Cham. https://doi.org/10.1007/978-3-031-20253-7_10
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