Road Networks Matching Supercharged with Embeddings

The increasing reliance on digital maps across various mobile applications highlights the critical need for integrating and reconciling map data from diverse sources, a process known as map conflation. A key challenge in map conflation is road network matching, which involves aligning road links between different datasets to ensure data interoperability. Traditional methods struggle with variations in geometric, semantic, and topological features between road networks, limiting their effectiveness. In this work, we introduce a novel Road Network Embeddings Matching (RNEM) method that leverages geometric, semantic, and topological embeddings to improve road network matching. Our approach, which incorporates advanced techniques such as pre-Trained models for feature extraction, demonstrates significant improvements in accuracy, precision, recall, and F1 scores across diverse datasets. This research directly contributes to addressing core challenges faced by the Overture Maps Foundation in their effort to create unified and interoperable open map data. The RNEM method provides a more efficient and reliable solution for map conflation, with broad implications for applications in navigation, autonomous driving, and urban planning.