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Multi-source System for Accurate Urban Extension Detection

  • Hassna KilaniEmail author
  • Hichem Ben Abdallah
  • Takoua Abdellatif
  • Rabah Attia
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

This paper proposed a novel observation system that is based on multi sources of collected data for urban extension detection. In addition to the satellite image processing, the evolution of unmanned aerial vehicle (UAV) technology created a practical data source for image classification and mapping. For the detected data analysis, storage and processing, a big data framework for urban extension detection was presented. In this Framework, Deep Learning (DL) algorithms were used for the classification and the analysis of multi source images.

Keywords

Very high resolution (VHR) images UAV Deep-learning Urban extension U-net architecture 

Notes

Acknowledgements

This work was conducted in collaboration with National Mapping and Remote Sensing Center (CNCT), in the context of the national project PRF on “Urban extension detection 2015–2018”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hassna Kilani
    • 1
    Email author
  • Hichem Ben Abdallah
    • 1
    • 2
  • Takoua Abdellatif
    • 1
  • Rabah Attia
    • 1
  1. 1.SERCOM Laboratory, Tunisia Polytechnic SchoolLa MarsaTunisia
  2. 2.Centre de Recherche Militaire CRML’AouinaTunisia

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