Deep Learning Classification of Building Types in Northern Cyprus


  • Mubarak Muhammad Near East University
  • Sertan Serte Near East University



Keyword: Deep learning; AlexNet; Neural Network; Northern Cyprus; Apartment; Villa, Real Estate.


Among the areas where AI studies centered on developing models that provide real-time solutions for the real estate industry are real estate price forecasting, building age, and types and design of the building (villa, apartment, floor number). Nevertheless, within the ML sector, DL is an emerging region with an Interest increases every year. As a result, a growing number of DL research are in conferences and papers, models for real estate have begun to emerge. In this study, we present a deep learning method for classification of houses in Northern Cyprus using Convolutional neural network.

This work proposes the use of Convolutional neural networks in the classification of houses images. The classification will be based on the house age, house price, number of floors in the house, house type i.e. Villa and Apartment.

The first category is Villa versus Apartments class; based on the training dataset of 362 images the class result shows the overall accuracy of 96.40%. The second category is split into two classes according to age of the buildings, namely 0 to 5 years Apartments 6 to 10 years Apartments. This class is to classify the building based on their age and the result shows the accuracy of 87.42%. The third category is villa with roof versus Villa without roof apartments class which also shows the overall accuracy of 87.60%. The fourth category is Villa Price from 10,000 euro to 200,000 Versus Villa Price from 200,000 Euro to above and the result shows the accuracy of 81.84%. The last category consists of three classes namely 2 floor Apartment versus 3 floor Apartment, 2 floor Apartment versus 4 floor Apartment and 2 floor Apartment versus 5 floor Apartment which all shows the accuracy of 83.54%, 82.48% and 84.77% respectively.

From the experiments carried out in this thesis and the results obtained we conclude that the main aims and objectives of this thesis which is to used Deep learning in Classification and detection of houses in Northern Cyprus and to test the performance of AlexNet for houses classification was successful. This study will be very significant in creation of smart cities and digitization of real estate sector as the world embrace the used of the vast power of Artificial Intelligence, machine learning and machine vision.

Author Biography

Sertan Serte, Near East University

Associate Professor

Department of Electrical and Electronics Engineering


G. Gilani, “ASSESSING FLEXIBILITY IN REAL ESTATE MASS HOUSING,” 2020, doi: 10.4013/arq.2020.161.09.

K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “A Brief Survey of Deep Reinforcement Learning,” pp. 1–16.

A. Henn, C. Römer, and G. Gröger, “Automatic classification of building types in 3D city models Using SVMs for semantic enrichment of low resolution building data,” pp. 281–306, 2012, doi: 10.1007/s10707-011-0131-x.

A. Henn, G. Gröger, V. Stroh, and L. Plümer, “ISPRS Journal of Photogrammetry and Remote Sensing Model driven reconstruction of roofs from sparse LIDAR point clouds,” vol. 76, pp. 17–29, 2013, doi: 10.1016/j.isprsjprs.2012.11.004.

Y. Li, Y. Chen, A. Rajabifard, K. Khoshelham, and M. Aleksandrov, “Estimating Building Age from Google Street View Images Using Deep Learning,” no. 40, pp. 1–7.

R. Sensing, S. I. Sciences, F. Biljecki, and M. Sindram, “ESTIMATING BUILDING AGE WITH 3D GIS,” vol. IV, no. October, pp. 26–27, 2017.

S. Serte, “A Generalized Deep Learning Model for Glaucoma Detection,” 2019.

A. Serener, “Transfer Learning for Early and Advanced Glaucoma Detection with Convolutional Neural Networks,” pp. 6–9.

A. H. Robertson, Yearbook of the European Convention on Human Rights/Annuaire de la Convention Europeenne des Droits de L’Homme: The European Commission and European Court of Human Rights/Commission et Cour Europeennes des Droits de L’Homme. Springer, 2013.

Ü. Alptekin and A. Ertacs, “Kuzey K{i}br{i}s Türk Cumhuriyeti’nde 1995 y{i}l{i} orman yang{i}n{i} sonras{i}ndaki a{u{g}}açland{i}rmalardan gözlemler,” {.I}stanbul Üniversitesi Orman Fakültesi Derg., vol. 43, no. 3–4, pp. 133–144, 1993.

“North Cyprus Map.”

M. Kiessel, D. Yücel-besİm, and A. Tozan, “THE NEW ARCHITECTURAL CLASSICISM IN,” 2011, doi: 10.4305/METU.JFA.2011.2.8.

A. Sivakumar, “Pelletized fly ash lightweight aggregate concrete: A promising material,” J. Civ. Eng. Constr. Technol., vol. 3, no. 2, pp. 42–48, 2012, doi: 10.5897/jbd11.088.

“101 Elver.” .

A. Uçar, Y. Demir, and C. Güzeliş, “Object recognition and detection with deep learning for autonomous driving applications,” Simulation, vol. 93, no. 9, pp. 759–769, 2017, doi: 10.1177/0037549717709932.

A. Vyas, S. Yu, and J. Paik, Fundamentals of digital image processing. 2018.

C. L. Bai and H. Liu, “Image enhancement based on wavelet transform with MATLA,” 2011 Int. Conf. Multimed. Technol. ICMT 2011, pp. 368–370, 2011, doi: 10.1109/ICMT.2011.6001955.

K. Satone, A. Deshmukh, and P. Ulhe, “A review of image compression techniques,” Proc. Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2017, vol. 2017-Janua, pp. 97–101, 2017, doi: 10.1109/ICECA.2017.8203651.

M. A. Aswathy and M. Jagannath, “Performance Analysis of Segmentation Algorithms for the Detection of Breast Cancer,” Procedia Comput. Sci., vol. 167, pp. 666–676, 2020, doi: 10.1016/j.procs.2020.03.333.

A. Krizhevsky and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” pp. 1–9.

G. H. Granlund, H. Knutsson, and R. Wilson, “Image enhancement.,” Fundam. Comput. Vis., pp. 57–67, 1983.

F. R. Mashrur, “Automatic Identification of Arrhythmia from ECG Using AlexNet Convolutional Neural Network,” no. December, pp. 20–22, 2019.

T. Kim, S. C. Suh, H. Kim, J. Kim, and J. Kim, “An Encoding Technique for CNN-based Network Anomaly Detection,” Proc. - 2018 IEEE Int. Conf. Big Data, Big Data 2018, pp. 2960–2965, 2019, doi: 10.1109/BigData.2018.8622568.

L. Mohammadpour, T. C. Ling, C. S. Liew, and C. Y. Chong, “A Convolutional Neural Network for Network Intrusion Detection System,” Proc. Asia-Pacific Adv. Netw., vol. 46, no. 0, pp. 50–55, 2018.

A. Burkov, “Machine Learning.”

E. Halim, P. P. Halim, and M. Hebrard, “Artificial Intelligent Models for Breast Cancer Early Detection,” Proc. 2018 Int. Conf. Inf. Manag. Technol. ICIMTech 2018, no. September, pp. 517–521, 2018, doi: 10.1109/ICIMTech.2018.8528140.

M. U. Hoque, T. M. S. Sazzad, A. K. M. A. Farabi, I. Hosen, and M. A. Somi, “An Automated Approach to Detect Breast Cancer Tissue Using Ultrasound Images,” 1st Int. Conf. Adv. Sci. Eng. Robot. Technol. 2019, ICASERT 2019, vol. 2019, no. Icasert, pp. 31–34, 2019, doi: 10.1109/ICASERT.2019.8934546.

K. V. Devarapu, S. Murala, and V. Kumar, “Denoising of ultrasound images using curvelet transform,” in 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010, vol. 3, pp. 447–451.

Y. Ebrahim, M. Ahmed, S. C. Chau, and W. Abdelsalam, “An efficient shape representation and description technique,” Proc. - Int. Conf. Image Process. ICIP, vol. 6, pp. 441–444, 2007, doi: 10.1109/ICIP.2007.4379616.

R. Srisha and A. Khan, “Morphological Operations for Image Processing : Understanding and its Applications,” NCVSComs-13, no. December, pp. 17–19, 2013.




How to Cite

Muhammad, M., & Serte, S. (2021). Deep Learning Classification of Building Types in Northern Cyprus. International Journal of Computer and Information Technology(2279-0764), 10(3).