The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W3-2021, 199–203, 2022
https://doi.org/10.5194/isprs-archives-XLVI-4-W3-2021-199-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W3-2021, 199–203, 2022
https://doi.org/10.5194/isprs-archives-XLVI-4-W3-2021-199-2022
 
11 Jan 2022
11 Jan 2022

THE USE OF DEEP LEARNING IN REMOTE SENSING FOR MAPPING IMPERVIOUS SURFACE: A REVIEW PAPER

S. Mahyoub1, H. Rhinane1, M. Mansour1, A. Fadil2, Y. Akensous3, and A. Al Sabri4 S. Mahyoub et al.
  • 1Laboratory Geosciences, Department of Geology, Faculty of Sciences, Hassan II University Casablanca, Morocco
  • 2Hassania School of Public Works, Morocco
  • 3Laboratory of Applied Geology, Geomatics and Environment, Department of Geology, Faculty of Sciences Ben M’sik, Hassan II University Casablanca, Morocco
  • 4Department of Physics, Laboratory of Engineering and Materials, Faculty of Sciences Ben M’sik, Hassan II University Casablanca, Morocco

Keywords: Impervious Surface, Remote Sensing, Deep Learning, CNNs, DNNs, Review

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.