# RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

arXiv: https://arxiv.org/abs/1912.12408 · HTML: https://mlsanity.com/paper/1912.12408/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [Graph Unlearning](https://mlsanity.com/paper/2103.14991/) (2021)
- [Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis](https://mlsanity.com/paper/2311.00164/) (2023)
- [Automatic Extraction of Road Networks from Satellite Images by using Adaptive Structural Deep Belief Network](https://mlsanity.com/paper/2110.12684/) (2021)
- [Learning to integrate vision data into road network data](https://mlsanity.com/paper/2112.10624/) (2021)
- [Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images](https://mlsanity.com/paper/2411.12620/) (2024)
- [Visual Feature Encoding for GNNs on Road Networks](https://mlsanity.com/paper/2203.01187/) (2022)
- [Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks](https://mlsanity.com/paper/2507.19964/) (2025)
- [Enhancing Interactive Voting-Based Map Matching: Improving Efficiency and Robustness for Heterogeneous GPS Trajectories](https://mlsanity.com/paper/2508.11235/) (2025)
- [Automatic Extraction of Road Networks by using Teacher-Student Adaptive Structural Deep Belief Network and Its Application to Landslide Disaster](https://mlsanity.com/paper/2511.05567/) (2025)
- [Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimation](https://mlsanity.com/paper/2512.02920/) (2025)
- [LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction](https://mlsanity.com/paper/2602.23290/) (2026)

## Abstract (arXiv)

Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused by a fundamental limitation -- the limited effective receptive field of image classifiers. To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers. We evaluate RoadTagger on both a large real-world dataset covering 688 km^2 area in 20 U.S. cities and a synthesized micro-dataset. In the evaluation, RoadTagger improves inference accuracy over the CNN image classifier based approaches. RoadTagger also demonstrates strong robustness against different disruptions in the satellite imagery and the ability to learn complicated inductive rules for aggregating scattered information along the road network.