City-Scale Lane-Level Mapping From Crowdsourced Trajectories and Satellite Imagery

Abstract

Lane-level maps are increasingly preferred over Standard-Definition (SD) and High-Definition (HD) maps, offering a better trade-off among detail richness, coverage breadth, and data freshness. However, constructing city-scale lane-level maps remains time-consuming and labor-intensive. To address these challenges, this letter presents an automated mapping framework that fuses crowdsourced trajectories with satellite imagery to enable scalable and accurate map generation. Our approach begins by mining billions of trajectories to extract the geometric and topological structure of road networks. To enrich feature representation, we introduce an effective multimodal fusion mechanism that integrates trajectory data with satellite images, leveraging the complementary strengths of both modalities. Furthermore, a spatiotemporal prior-fusion decoding strategy is proposed to enhance the accuracy and consistency of vectorized map element perception. Finally, a globally consistent, vectorized lane-level map is generated by synthesizing the geometric and semantic output of the perception pipeline. The proposed method achieves the state-of-the-art mean average precision (mAP) on both a large-scale self-curated dataset and Argoverse 2. Having processed over one million kilometers of road networks, the system demonstrates significant scalability and practicality for real-world lane-level mapping applications.