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

Abstract

The buildings in the rural areas of Morocco exist in various shapes and sizes. They are randomly distributed and are generally constructed of primary materials such as clay, wood, and tin. For these reasons, their detection is generally difficult and inaccurate with optical satellite imagery and traditional image processing techniques, particularly in rural settlements. New approaches, particularly those of Deep Learning, are called for testing their contribution to the detection of buildings and settlements in rural areas. This study aims to detect and map the settlements in rural areas in the Souss-Massa region using Sentinel-2 satellite images, based on deep Learning algorithms. First, we tested the result of the convolutional neural network architecture UNet. Then, to evaluate the impact of filters number on the performance of UNet, we increased the number of filters in the convolution layer. And third, we implement the deep Residual UNet (ResUNet). To evaluate the quality of tested models, special metrics, such as accuracy, precision, recall, F1-score, and the ROC curve are used. The obtained precision of 87% of precision and 54% of F1-score for the UNet with an increased number of filters outperforms the other algorithms UNet and ResUNet, which have 86.2% of precision and 81.2% precision, respectively. We compared our perception to those of other related studies conducted to extract buildings or settlements in rural areas using high to very high-resolution images and machine learning and deep learning algorithms. Our results show that the performance of settlement detection in rural areas using deep learning is affected by the quality and quantity of the model training base images and the number of filters used in the convolution layer.