The Classification of Cultural Heritage Buildings in Athens Using Deep Learning Techniques

Heritage

article

Konstantina Siountri¹  Christos-Nikolaos Anagnostopoulos² 

[1] Digital Culture, Smart Cities, IoT & Advanced Digital Technologies, Department of Informatics, University of Piraeus;Cultural Technology and Communication Department, University of the Aegean

关键词: classification;    deep learning;    YOLO;    building typology;   

DOI  :  10.3390/heritage6040195

学科分类：医学（综合）

来源: mdpi

【 摘 要 】

Architectural structures, the basic elements of the urban web, are an aggregation of buildings that have been built at different times, with different materials, and in different styles. Through research, they can be divided into groups that present common morphological attributes and refer to different historical periods with particular social, economic, and cultural characteristics. The identification of these common repeating elements and organizational construction structures leads to the identification of the “type” of the building, which until now has required specialized knowledge, time, and customized proof checking. Recent developments in the field of artificial intelligence (AI) and, more specifically, in deep learning (DL) appear to contribute gradually to the study of the typological evolution of buildings, especially those of cultural heritage (CH). In this paper, we present a deep-learning-based method for the classification of modern Athenian architecture (since 1830) using the YOLO algorithm. This research work can contribute to the digital management of the existing urban building stock, the autonomous large-scale categorization of data that are available from street view images, and the enhancement of the tangible CH.

【 授权许可】

CC BY