Bridging the Gap Between Sparsity and Redundancy: A Dual-Decoding Framework with Global Context for Map Inference

• URL: http://arxiv.org/abs/2509.11731v1
• Date: Mon, 15 Sep 2025 09:31:38 GMT
• Title: Bridging the Gap Between Sparsity and Redundancy: A Dual-Decoding Framework with Global Context for Map Inference
• Authors: Yudong Shen, Wenyu Wu, Jiali Mao, Yixiao Tong, Guoping Liu, Chaoya Wang,
• Abstract summary: We propose DGMap, a dual-decoding framework with global context awareness.<n>By integrating global semantic context with local geometric features, DGMap improves keypoint detection accuracy.<n>Global Context-aware Relation Prediction module suppresses false connections in dense-trajectory regions.
• Score: 1.6891753537675143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Trajectory data has become a key resource for automated map in-ference due to its low cost, broad coverage, and continuous availability. However, uneven trajectory density often leads to frag-mented roads in sparse areas and redundant segments in dense regions, posing significant challenges for existing methods. To address these issues, we propose DGMap, a dual-decoding framework with global context awareness, featuring Multi-scale Grid Encoding, Mask-enhanced Keypoint Extraction, and Global Context-aware Relation Prediction. By integrating global semantic context with local geometric features, DGMap improves keypoint detection accuracy to reduce road fragmentation in sparse-trajectory areas. Additionally, the Global Context-aware Relation Prediction module suppresses false connections in dense-trajectory regions by modeling long-range trajectory patterns. Experimental results on three real-world datasets show that DGMap outperforms state-of-the-art methods by 5% in APLS, with notable performance gains on trajectory data from the Didi Chuxing platform

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