The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Jan 2022)

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

  • S. Mahyoub,
  • H. Rhinane,
  • M. Mansour,
  • A. Fadil,
  • Y. Akensous,
  • A. Al Sabri

DOI
https://doi.org/10.5194/isprs-archives-XLVI-4-W3-2021-199-2022
Journal volume & issue
Vol. XLVI-4-W3-2021
pp. 199 – 203

Abstract

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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.