Deep Learning in Historical Architecture Remote Sensing: Automated Historical Courtyard House Recognition in Yazd, Iran

Hadi Yazdi, Shina Sad Berenji, Ferdinand Ludwig, Sajad Moazen

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This research paper reports the process and results of a project to automatically classify historical and non-historical buildings using airborne and satellite imagery. The case study area is the center of Yazd, the most important historical site in Iran. New computational scientific methods and accessibility to satellite images have created more opportunities to work on automated historical architecture feature recognition. Building on this, a convolutional neural network (CNN) is the main method for the classification task of the project. The most distinctive features of the historical houses in Iran are central courtyards. Based on this characteristic, the objective of the research is recognizing and labeling the houses as historical buildings by a CNN model. As a result, the trained model is tested by a validation dataset and has an accuracy rate of around 98%. In Sum, the reported project is one of the first works on deep learning methods in historical Iranian architecture study and one of the first efforts to use automated remote sensing techniques for recognizing historical courtyard houses in aerial images.

Original languageEnglish
Pages (from-to)3066-3080
Number of pages15
JournalHeritage
Volume5
Issue number4
DOIs
StatePublished - Dec 2022

Keywords

  • Yazd
  • convolutional neural network (CNNs)
  • deep learning
  • historical architecture
  • image processing
  • remote sensing

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