A Deep Learning Classification Approach Using High Spatial Satellite Images for Detection of Built-Up Areas in Rural Zones: Case Study of Souss-Massa Region, Morocco

The buildings in the rural areas of Morocco exist in various shapes and sizes and are generally constructed of primary materials such as clay, wood, and tin. Their detection is difficult and often inaccurate with traditional optical satellite imagery and image processing techniques, especially in rural settlements. This study aims to detect and map the settlements in rural areas of the Souss-Massa region using Sentinel-2 satellite images and deep learning algorithms. The baseline architecture tested is the convolutional neural network UNet; its performance was evaluated, the number of filters in the convolution layers was increased, and a deep Residual UNet (ResUNet) was also implemented. Model quality was assessed using precision, recall, F1-score, and ROC curve. Results showed the UNet with increased filters outperformed other models, achieving 87\% precision and 54\% F1-score. A comparison with other related studies using different machine learning and deep learning algorithms is presented. Findings highlight that deep learning performance for settlement detection in rural areas depends strongly on the number and quality of training images, as well as network design parameters such as the number of filters in convolutional layers.