Unveiling Annual Dynamics in Large-Scale Road Networks Through a Connectivity-Aware Approach Utilizing Sentinel-2 Multi-Spectral Imagery-Reference-Cited by-同舟云学术

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Unveiling Annual Dynamics in Large-Scale Road Networks Through a Connectivity-Aware Approach Utilizing Sentinel-2 Multi-Spectral Imagery

Published:2024-07-07 Issue: Volume: Page:9535-9538
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Zhang Lixian¹,Du Kangrui²,Yuan Shuai³,Dong Runmin⁴,Zheng Juepeng²,Fu Haohuan⁴

Affiliation:

1. National Supercomputing Center in Shenzhen,High Performance Computing Department,Shenzhen,China

2. Sun Yat-Sen University,School of Artificial Intelligence,Zhuhai,China

3. The University of Hong Kong,Department of Geography,Hong Kong,China

4. Tsinghua University,Department of Earth System Science,Beijing,China

Funder

Research and Development

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642887.pdf?arnumber=10642887

Reference6 articles.

1. DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing Data

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

3. SWCARE: Switchable learning and connectivity-aware refinement method for multi-city and diverse-scenario road mapping using remote sensing images

4. Srbuildingseg-E²: An Integrated Model for End-to-End Higher-Resolution Building Extraction

5. Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction

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