Automatic extraction and 3D modeling of real road scenes using UAV imagery and deep learning semantic segmentation-Reference-Cited by-同舟云学术

Automatic extraction and 3D modeling of real road scenes using UAV imagery and deep learning semantic segmentation

Published:2024-06-19 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Liu Zhen¹²,Wu Wenxiu³,Wang Danyu¹,Cui Bingyan⁴,Gu Xingyu¹

Affiliation:

1. Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing, People’s Republic of China

2. Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA

3. Highway and Transportation Management Center, Jinhua, People’s Republic of China

4. Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

Funder

Jinhua Science and Technology Bureau

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2365970

Reference54 articles.

1. ASSESSING THE SPATIAL ACCURACY OF UAV-DERIVED PRODUCTS BASED ON VARIATION OF FLIGHT ALTITUDES

2. Investigation of Accuracy of Detailed Verified by Unmanned Aerial Vehicles with RTK System; The Example of Ortakent-Bodrum Area;Altuntabak Hazal;Review of. Advanced UAV,2022

3. Performance analysis of rule-based classification and deep learning method for automatic road extraction

4. A Fast Inference Vision Transformer for Automatic Pavement Image Classification and Its Visual Interpretation Method

5. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

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