Cultural Heritage Analysis With Object Recognition Method: the Example of Konya

dc.contributor.author Dönmez, Mustafa Alper
dc.contributor.author Ulusoy, Mine
dc.date.accessioned 2024-02-27T05:43:31Z
dc.date.available 2024-02-27T05:43:31Z
dc.date.issued 2021
dc.description.abstract Cultural heritage is very important in order to today's observe the sociological, economic and technological accumulations of the past societies and to benefit from these savings. Due to this importance, it is a duty of today's society to ensure the best preservation of the cultural heritage, which we will leave to future generations. Process for the protection of cultural heritage is a holistic approach consisting of research, documentation, analysis, diagnosis and determination of the conservation approach. One of the most important stages of conservation studies is the accurate analysis of cultural heritage through reliable documents obtained as a result of extensive research. Efforts to obtain more accurate results in the analysis of cultural heritage have caused different disciplines to work together and develop new techniques to be used in this field. One of the newest techniques used in cultural heritage studies is object recognition methods, the use of which has become widespread in many areas in recent years. The use of object recognition methods allows to obtain more precise, reliable and faster results than traditional methods. An important part of the cultural heritage consists of immovable cultural assets. Especially the houses where societies spend most of their lives is a database that can reflect the characteristics of the period they were built in from various angles. In this study, a residential facade typology algorithm (RFTA) trained by object recognition method, using facede images of 19th and 20th century Konya houses, was created. The facade typology algorithm works as an artificial expert who can predict the style or period in which the houses are built by analyzing the facade characters through the photographs of the houses. In order to test RFTA, photographs of 10 residential buildings in Konya city are presented to the algorithm. According to the results obtained from this study, it is seen that RFTA works with great accuracy without any expert help. This study has been prepared with the aim of providing an innovative basis for future cultural heritage studies. en_US
dc.identifier.isbn 9786057458216 en_US
dc.identifier.uri https://hdl.handle.net/20.500.13091/5165
dc.language.iso en en_US
dc.publisher BABIL Yayınevi en_US
dc.relation 4. International Istanbul Scientific Research Congress April 2-4, 2021 Istanbul Turkey en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Artificial intelligence en_US
dc.subject Object recognition en_US
dc.subject Deep learning en_US
dc.subject Convolutional neural network en_US
dc.subject Traditional residence en_US
dc.subject Object detection en_US
dc.title Cultural Heritage Analysis With Object Recognition Method: the Example of Konya en_US
dc.type Conference Object en_US
dspace.entity.type Publication
gdc.author.id 0000-0002-7765-9888
gdc.author.id 0000-0002-7475-7511
gdc.author.institutional Dönmez, Mustafa Alper
gdc.author.institutional Ulusoy, Mine
gdc.coar.access open access
gdc.coar.type text::conference output
gdc.contributor.affiliation Fakülteler, Mimarlık ve Tasarım Fakültesi, Mimarlık Bölümü en_US
gdc.contributor.affiliation Fakülteler, Mimarlık ve Tasarım Fakültesi, Mimarlık Bölümü en_US
gdc.description.department Fakülteler, Mimarlık ve Tasarım Fakültesi, Mimarlık Bölümü en_US
gdc.description.endpage 360 en_US
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.startpage 360 en_US
gdc.description.wosquality N/A
gdc.virtual.author Dönmez, Mustafa Alper
gdc.virtual.author Ulusoy, Mine
relation.isAuthorOfPublication d1baf089-b1b5-4274-9bca-9f6da27bebd8
relation.isAuthorOfPublication 5a8cf4a1-6dc0-4af8-9236-fa98e09fb7d0
relation.isAuthorOfPublication.latestForDiscovery d1baf089-b1b5-4274-9bca-9f6da27bebd8

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