Development of Deep Learning Pavement Extraction and Crack Segmentation Algorithms to Analyze UAV Images of Roadways-Reference-Cited by-同舟云学术

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Development of Deep Learning Pavement Extraction and Crack Segmentation Algorithms to Analyze UAV Images of Roadways

Published:2024-09 Issue:3 Volume:30 Page:
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Moon Byungkyu¹^ORCID,Lee Hosin “David”²^ORCID

Affiliation:

1. Staff Engineer, Applied Research Associates, 100 Trade Ctr Dr., Champaign, IL 61820; Dept. of Civil and Environmental Engineering, Univ. of Iowa, Iowa City, IA 52242 (corresponding author). ORCID: .

2. Professor, Dept. of Civil and Environmental Engineering, Univ. of Iowa, Iowa City, IA 52242. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JITSE4.ISENG-2351

Reference34 articles.

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

2. Aleti T. R. 2014. “Developing a pavement management system using data fusion of physical features.” Master of Science thesis Dept. of Civil and Environmental Engineering Northeastern Univ.

3. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks

4. Arnberg, P., M. Burke, M. Georg, R. Oberholtzer, K. Rahs, and L. Sjogren. 1991. The laser RST: Current status. Linköping, Sweden: Swedish National Road and Transport Research Institute.

5. Chevalier G. 2017. “Smoothly-blend-image-patches.” Accessed October 5 2022. https://github.com/Vooban/Smoothly-Blend-Image-Patches.

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