The Use of Deep Learning in Remote Sensing for Mapping Impervious Surface: A Review Paper

Abstract

Abstract. In recent years, deep convolutional neural networks (CNNs) algorithms have demonstrated outstanding performance in a wide range of remote sensing applications, including image classification, image detection, and image segmentation. Urban development, as defined by urban expansion, mapping impervious surfaces, and built-up areas, is one of these fascinating issues. The goal of this research is to explore at and summarize the deep learning approaches used in urbanization. In addition, several of these methods are highlighted in order to provide a comprehensive overview and comprehension of them, as well as their pros and downsides.

Impervious Surface Prediction in Marrakech City using Artificial Neural Network

“…Remote sensing has been widely utilized to identify impervious surfaces. Several articles have been published describing the state-of-the-art of this topic [13][14][15][16][17][18][19][20][21]. Earlier, statistical remote sensing indices including the Normalized Difference Built-up Index (NDBI) [22], Normalized Difference Impervious Surface Index (NDISI) [23], modified NDISI [24], and perpendicular impervious surface index(PISI) [25] , biophysical composition index (BCI) [26] , and the normalized difference vegetation index (NDVI) have been developed to map impervious surfaces.…”