RoadVecNet: a new approach for simultaneous road network segmentation and vectorization from aerial and google earth imagery in a complex urban set-up-Reference-Cited by-同舟云学术

RoadVecNet: a new approach for simultaneous road network segmentation and vectorization from aerial and google earth imagery in a complex urban set-up

Published:2021-08-30 Issue:7 Volume:58 Page:1151-1174
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Abdollahi Abolfazl¹,Pradhan Biswajeet¹²^ORCID,Alamri Abdullah³

Affiliation:

1. University of Technology Sydney (UTS), CAMGIS, Sydney, Australia

2. Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Sekangor, Malaysia

3. Department of Geology & Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia

Funder

University of Technology Sydney

King Saud University

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2021.1972713

Reference53 articles.

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2. VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction From High-Resolution Remote Sensing Data

3. Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

4. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

5. Buslaev, A., S. Seferbekov, V. Iglovikov, and A. Shvets. 2018. “Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops: 207–210. Salt Lake City, UT, USA.

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