GLD-Road:A global-local decoding road network extraction model for remote sensing images

TL;DR

GLD-Road is a novel two-stage deep learning model that efficiently extracts detailed road networks from remote sensing images by combining global detection with local refinement, outperforming existing methods in accuracy and speed.

Contribution

The paper introduces GLD-Road, a two-stage model that integrates global node detection with local iterative refinement for improved road network extraction.

Findings

Outperforms state-of-the-art methods in accuracy (APLS)

Reduces retrieval time significantly (up to 92%)

Effective in both city-scale and space-based datasets

Abstract

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.