@misc{YazdiBanachMoazenetal., author = {Yazdi, Hadi and Banach, Marzena and Moazen, Sajad and Nadolny, Adam and Starke, Rolf and Bazazzadeh, Hassan}, title = {Central Courtyard Feature Extraction in Remote Sensing Aerial Images Using Deep Learning: A Case-Study of Iran}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {23}, issn = {2072-4292}, abstract = {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 K{\"o}ppen climate zones. Furthermore, these features were gathered in an extensive dataset, with 26,437 samples and 76 geometric and climactic 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.}, language = {en} } @misc{YazdiVukorepBazazzadeh, author = {Yazdi, Hadi and Vukorep, Ilija and Bazazzadeh, Hassan}, title = {The Methods of Deep Learning and Big Data Analysis in Promoting Sustainable Architecture}, series = {IOP Conference Series Earth and Environmental Science}, journal = {IOP Conference Series Earth and Environmental Science}, publisher = {IOP Publishing}, address = {Bristol}, doi = {10.1088/1755-1315/1078/1/012136}, pages = {10}, language = {en} }