GLD-Road:A global-local decoding road network extraction model for remote sensing images
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Abstract (arXiv)
Road networks are crucial for mapping, autonomous driving, and disaster response. While manual annotation is costly, deep learning offers efficient extraction. Current methods include postprocessing (prone to errors), global parallel (fast but misses nodes), and local iterative (accurate but slow). We propose GLD-Road, a two-stage model combining global efficiency and local precision. First, it detects road nodes and connects them via a Connect Module. Then, it iteratively refines broken roads using local searches, drastically reducing computation. Experiments show GLD-Road outperforms state-of-the-art methods, improving APLS by 1.9% (City-Scale) and 0.67% (SpaceNet3). It also reduces retrieval time by 40% vs. Sat2Graph (global) and 92% vs. RNGDet++ (local). The experimental results are available at https://github.com/ucas-dlg/GLD-Road.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 30 of its 56 references that are in the map. First posted on arXiv 11 Jun 2025.
Builds on
30 of 56 references.
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18 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.