Detection and Classification of Vegetation for Roadside Vegetation Inspection and Rehabilitation Using Deep Learning Techniques-Reference-Cited by-同舟云学术

Detection and Classification of Vegetation for Roadside Vegetation Inspection and Rehabilitation Using Deep Learning Techniques

Published:2022-08-31 Issue: Volume: Page:
ISSN:
Container-title:International Conference on Transportation and Development 2022
language:
Short-container-title:

Author:

Baral Anil¹²¹³⁴,Sadegh Nasr Mohammad¹²¹³⁴,Darghiasi Pooya¹²¹³⁴,Abediniangerabi Bahram¹²¹³⁴,Shahandashti Mohsen¹²¹³⁴

Affiliation:

1. Graduate Student, Dept. of Civil Engineering, Univ. of Texas at Arlington, Arlington, TX.

2. Graduate Student, Dept. of Computer Science and Engineering, Univ. of Texas at Arlington, Arlington, TX.

3. Assistant Professor, Dept. of Engineering and Technology, Texas A&M Univ.–Commerce, Commerce, TX.

4. Associate Professor, Dept. of Civil Engineering, Univ. of Texas at Arlington, Arlington, TX

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784484319.014

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3. Vegetation detection using deep learning and conventional methods;Ayhan B.;Remote Sensing,2020

4. Roadside vegetation design characteristics that can improve local, near-road air quality;Baldauf R.;Transportation research part D: Transport and environment,2017

5. A Data Integration Approach for Assessment of Rainfall-Induced Slope Failure Susceptibility

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