Central Courtyard Feature Extraction in Remote Sensing Aerial Images Using Deep Learning: A Case-Study of Iran
[ 1 ] Instytut Architektury i Planowania Przestrzennego, Wydział Architektury, Politechnika Poznańska | [ 2 ] Instytut Architektury, Urbanistyki i Ochrony Dziedzictwa, Wydział Architektury, Politechnika Poznańska | [ P ] employee | [ SzD ] doctoral school student
2021
scientific article
english
- central courtyard
- deep learning
- features extraction
- data analysis
- Iran
EN Central courtyards are primary components of vernacular architecture in Iran. The directions, dimensions, ratios, and other characteristics of central courtyards are critical for studying historical passive cooling and heating solutions. Several studies on central courtyards have compared their features in different cities and climatic zones in Iran. In this study, deep learning methods for object detection and image segmentation are applied to aerial images, to extract the features of central courtyards. The case study explores aerial images of nine historical cities in Bsk, Bsh, Bwk, and Bwh Köppen climate zones. Furthermore, these features were gathered in an extensive dataset, with 26,437 samples and 76 geometric and climatic features. Additionally, the data analysis methods reveal significant correlations between various features, such as the length and width of courtyards. In all cities, the correlation coefficient between these two characteristics is approximately +0.88. Numerous mathematical equations are generated for each city and climate zone by fitting the linear regression model to these data in different cities and climate zones. These equations can be used as proposed design models to assist designers and researchers in predicting and locating the best courtyard houses in Iran’s historical regions.
4843-1 - 4843-20
article number: 4843
CC BY (attribution alone)
open journal
final published version
at the time of publication
public
100
100
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