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Found 3 papers, 1 papers with code
Visual Feature Encoding for GNNs on Road Networks
In this work, we present a novel approach to learning an encoding of visual features into graph neural networks with the application on road network data.
Learning to integrate vision data into road network data
Road networks are the core infrastructure for connected and autonomous vehicles, but creating meaningful representations for machine learning applications is a challenging task.
Graph Representation Learning for Road Type Classification
We present a novel learning-based approach to graph representations of road networks employing state-of-the-art graph convolutional neural networks.
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