Road Extraction from Remotely Sensed Data: A Review

Authors

Mohd Jawed Khan
Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, India
Pankaj Pratap Singh
Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, India

Synopsis

Up-to-date road networks are crucial and challenging in computer vision tasks. Road extraction is yet important for vehicle navigation, urban-rural planning, disaster relief, traffic management, road monitoring and others. Road network maps facilitate a great number of applications in our everyday life. Therefore, a systematic review of deep learning approaches applied to remotely sensed imagery for road extraction is conducted in this paper. Four main types of deep learning approaches, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models are presented in this paper. We also compare these various deep learning models applied to remotely sensed imagery to show their performances in extracting road parts from high-resolution remote sensed imagery. Later future research directions and research gaps are described.

ICTCon2021
Published
July 12, 2021
Online ISSN
2582-3922