SC-RoadDeepNet: A New Shape and Connectivity-Preserving Road Extraction Deep Learning-Based Network From Remote Sensing Data-Reference-Cited by-同舟云学术

SC-RoadDeepNet: A New Shape and Connectivity-Preserving Road Extraction Deep Learning-Based Network From Remote Sensing Data

Published:2022 Issue: Volume:60 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Abdollahi Abolfazl¹^ORCID,Pradhan Biswajeet¹^ORCID,Alamri Abdullah²

Affiliation:

1. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW, Australia

2. Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia

Funder

Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia

Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09684511.pdf?arnumber=9684511

Reference43 articles.

1. A skeletonization algorithm by maxima tracking on Euclidean distance transform

2. A 3-subiteration 3D thinning algorithm for extracting medial surfaces

3. Beyond the Pixel-Wise Loss for Topology-Aware Delineation

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

5. Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss

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