International Journal of Applied Earth Observations and Geoinformation (Aug 2022)

Road extraction in remote sensing data: A survey

  • Ziyi Chen,
  • Liai Deng,
  • Yuhua Luo,
  • Dilong Li,
  • José Marcato Junior,
  • Wesley Nunes Gonçalves,
  • Abdul Awal Md Nurunnabi,
  • Jonathan Li,
  • Cheng Wang,
  • Deren Li

Journal volume & issue
Vol. 112
p. 102833

Abstract

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Automated extraction of roads from remotely sensed data come forth various usages ranging from digital twins for smart cities, intelligent transportation, urban planning, autonomous driving, to emergency management. Many studies have focused on promoting the progress of methods for automated road extraction from aerial and satellite optical images, synthetic aperture radar (SAR) images, and LiDAR point clouds. In the past 10 years, no a more comprehensive survey on this topic could be found in literature. This paper attempts to provide a comprehensive survey on road extraction methods that use 2D earth observing images and 3D LiDAR point clouds. In this review, we first present a tree-structure that separate the literature into 2D and 3D. Then, further methodologies level classification is demonstrated both in 2D and 3D. In 2D and 3D, we introduce and analyze the literature published in the last ten years. Except for the methodologies, we also review the aspects of data commonly used. Finally, this paper explores the existing challenges and future trends.

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