Optimal Deep Transfer Learning-Based Road Extraction for Intelligent Transportation Systems Using High-Resolution Remote Sensing Imagery-Reference-Cited by-同舟云学术

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Optimal Deep Transfer Learning-Based Road Extraction for Intelligent Transportation Systems Using High-Resolution Remote Sensing Imagery

Published:2023-07-15 Issue: Volume: Page:
ISSN:
Container-title:2023 6th International Conference on Engineering Technology and its Applications (IICETA)
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Author:

Al Sadi Hafidh I.¹,Sabah Hawraa Ali²,Kurdi Waleed Hadi Madhloom³,Najm Nahlah M.A.M.⁴,Alazzai Waleed Khalid⁵,Alzubaidi Laith H.⁶

Affiliation:

1. College of MLT, Ahl Al Bayt University,karbala,Iraq

2. National University of Science and Technology,Dhi Qar,Iraq

3. Altoosi University College,Najaf,Iraq

4. Al Rafidain University College,Computer Technology Engineering Department,Bagdad,Iraq

5. Medical Technical College, Al-Farahidi University,Baghdad,Iraq

6. The Islamic university,Najaf,Iraq

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10351190/10351191/10351451.pdf?arnumber=10351451

Reference20 articles.

1. Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review

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

3. Split Depth-wise Separable Graph-Convolution Network for Road Extraction in Complex Environments from High-resolution Remote-Sensing Images

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

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

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