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Automatic Road Extraction with Multi-Source Data Revisited: Completeness, Smoothness and Discrimination

Summary: End-to-end revisit of multi-source road extraction: dual encoders fused with attention to enhance inter-source complementarity, plus an edge-prediction auxiliary task to yield smoother, continuous road masks. Introduces pixel-aware contrastive learning to suppress road-like false positives and a model-agnostic auxiliary-task pretraining to boost accuracy; validated on real datasets. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13140
Venue
VLDB
Year
2023
Pagerank
4.1945683e-05
Overall Rank
11,253 | 21.72%
DOI
10.14778/3611479.3611504

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Rank Citing Paper Year Venue Pagerank
10,083 GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases 2026 SIGMOD 4.1945683e-05
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