Deep Learning in Historical Architecture Remote Sensing
Automated Historical Courtyard House Recognition in Yazd, Iran
Bibliographic Data
| ID | 13124686 |
|---|---|
| Authors | Hadi Yazdi (0000-0002-1429-5642, Technical University of Munich, corresponding author), Shina Sad Berenji (0000-0003-1211-0533, Tarbiat Modares University), Ferdinand Ludwig (0000-0001-5877-5675, Technical University of Munich), Sajad Moazen (0000-0003-2999-7652, Iran University of Science and Technology) |
| Year | 2022 |
| Volume | 5 |
| Issue | 4 |
| Pages | 3066-3080 |
| Publication date | 2022-10-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Heritage (JOURNAL) |
| Journal identifiers | ISSN: 2571-9408 • E-ISSN: 2571-9408 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/heritage5040159 |
| OpenAlex | W4304806919 |
| Language | EN |
| Citations received | 2 |
| References cited | 41 |
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
Archaeology · Convolutional neural network · Deep learning · Feature (linguistics · Geography · Process (computing · Remote sensing · Satellite · Satellite imagery · Systems engineering · Task (project management · Archaeological Research and Protection · Computer Science · Engineering · Remote Sensing and LiDAR Applications · Remote-Sensing Image Classification · Architecture · Artificial Intelligence
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The data explosion
| Unique citing works | 2 |
|---|---|
| Citations per year | 1 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |