Reciprocal style and information transfer between historical Istanbul Pervititch Maps and satellite views using machine learning

Autores/as

DOI:

https://doi.org/10.18537/est.v011.n022.a06

Palabras clave:

Istanbul Pervititch Maps, artificial intelligence, machine learning, semantic segmentation, CycleGAN

Resumen

Historical maps contain significant data on the cultural, social, and urban character of cities. However, most historical maps utilize specific notation methods that differ from those commonly used today and converting these maps to more recent formats can be highly labor-intensive. This study is intended to demonstrate how a machine learning (ML) technique can be used to transform old maps of Istanbul into spatial data that simulates modern satellite views (SVs) through a reciprocal map conversion framework. With this aim, the Istanbul Pervititch Maps (IPMs) made by Jacques Pervititch in 1922-1945 and current SVs were used to test and evaluate the proposed framework. The study consists of a style and information transfer in two stages: (i) from IPMs to SVs, and (ii) from SVs to IPMs using CycleGAN (a type of generative adversarial network). The initial results indicate that the proposed framework can transfer attributes such as green areas, construction techniques/materials, and labels/tags.

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Biografía del autor/a

Sema Alaçam, Istanbul Technical University

Department of Architecture, Istanbul Technical University, Istanbul, Turkey

Ilker Karadag, Manisa Celal Bayar University

Department of Architecture, Manisa Celal Bayar University, Manisa, Turkey

Orkan Zeynel Güzelci, University of Porto

Faculty of Architecture + DFL/CEAU, University of Porto, Porto, Portugal

Citas

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Publicado

2022-07-25

Cómo citar

Alaçam, S., Karadag, I., & Güzelci, O. Z. (2022). Reciprocal style and information transfer between historical Istanbul Pervititch Maps and satellite views using machine learning. Estoa. Revista De La Facultad De Arquitectura Y Urbanismo De La Universidad De Cuenca, 11(22). https://doi.org/10.18537/est.v011.n022.a06