Open Access

ARTICLE

Desertification Detection in Makkah Region based on Aerial Images Classification

by Yahia Said^1,2,*, Mohammad Barr¹, Taoufik Saidani^2,3, Mohamed Atri^2,4

1 Electrical Engineering Department, College of Engineering, Northern Border University, Arar, Saudi Arabia
2 Laboratory of Electronics and Microelectronics (LR99ES30), Faculty of Sciences of Monastir, University of Monastir, Tunisia
3 Department of Computer Sciences, Faculty of Computing & Information Technology, Northern Border University, Rafha, Saudi Arabia
4 College of Computer Sciences, King Khalid University, Abha, Saudi Arabia

* Corresponding Author: Yahia Said. Email:

Computer Systems Science and Engineering 2022, 40(2), 607-618. https://doi.org/10.32604/csse.2022.018479

Received 10 March 2021; Accepted 05 May 2021; Issue published 09 September 2021

Abstract

Desertification has become a global threat and caused a crisis, especially in Middle Eastern countries, such as Saudi Arabia. Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification. The vegetation area in Makkah has been damaged because of desertification through wind, floods, overgrazing, and global climate change. The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area. In this paper, we propose an automatic desertification detection system based on Deep Learning techniques. Aerial images are classified using Convolutional Neural Networks (CNN) to detect land state variation in real-time. CNNs have been widely used for computer vision applications, such as image classification, image segmentation, and quality enhancement. The proposed CNN model was trained and evaluated on the Arial Image Dataset (AID). Compared to state-of-the-art methods, the proposed model has better performance while being suitable for embedded implementation. It has achieved high efficiency with 96.47% accuracy. In light of the current research, we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.

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