Road Extraction Based on Deep Learning Using Sdgsat-1 Nighttime Light Data-Reference-Cited by-同舟云学术

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Road Extraction Based on Deep Learning Using Sdgsat-1 Nighttime Light Data

Published:2024-07-07 Issue: Volume: Page:8033-8036
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Xie Qiyuan¹,Li Hui²,Jing Linhai²,Zhang Kongwen³

Affiliation:

1. Fuzhou University,Zijin School of Geology and Mining,Fuzhou,China,350108

2. International Research Center of Big Data for Sustainable Development Goals,Beijing,China,100094

3. University of the Fraser Valley,School of Computing,Abbotsford,Canada,V2S 7M7

Funder

National Key Research and Development Program of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10640732.pdf?arnumber=10640732

Reference11 articles.

1. Global Sustainable Development Report 2019

2. Road Extraction by Deep Residual U-Net

3. Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference

4. Road Network Extraction via Aperiodic Directional Structure Measurement

5. RADANet: Road Augmented Deformable Attention Network for Road Extraction From Complex High-Resolution Remote-Sensing Images

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