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.
S. Mahyoub1, H. Rhinane1, M. Mansour1, A. Fadil2, Y. Akensous3, and A. Al Sabri4
- 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
- 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
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Keywords: Impervious Surface, Remote Sensing, Deep Learning, CNNs, DNNs, Review
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.
