MCTN-Net: A Multiclass Transportation Network Extraction Method Combining Orientation and Semantic Features
Author:
Affiliation:
1. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
2. School of Resource and Environmental Sciences and the Collaborative Innovation Center for Geospatial Technology, Wuhan University, Wuhan, China
Funder
National Natural Science Foundation of China
Key Research and Development Program of Hubei Province
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/8859/10365397/10456894.pdf?arnumber=10456894
Reference19 articles.
1. Road Extraction by Deep Residual U-Net
2. Multi-Scale and Multi-Task Deep Learning Framework for Automatic Road Extraction
3. A Semantics-Geometry Framework for Road Extraction From Remote Sensing Images
4. SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
5. Improved Road Connectivity by Joint Learning of Orientation and Segmentation
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