# Learning to integrate vision data into road network data

arXiv: https://arxiv.org/abs/2112.10624 · HTML: https://mlsanity.com/paper/2112.10624/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing](https://mlsanity.com/paper/2007.06277/) (2020)
- [Relational Fusion Networks: Graph Convolutional Networks for Road Networks](https://mlsanity.com/paper/2006.09030/) (2020)
- [RoadTagger: Robust Road Attribute Inference with Graph Neural Networks](https://mlsanity.com/paper/1912.12408/) (2019)
- [Graph Representation Learning for Road Type Classification](https://mlsanity.com/paper/2107.07791/) (2021)

## Built on by (most cited first, 1 of 1)

- [Visual Feature Encoding for GNNs on Road Networks](https://mlsanity.com/paper/2203.01187/) (2022)

## Abstract (arXiv)

Road networks are the core infrastructure for connected and autonomous vehicles, but creating meaningful representations for machine learning applications is a challenging task. In this work, we propose to integrate remote sensing vision data into road network data for improved embeddings with graph neural networks. We present a segmentation of road edges based on spatio-temporal road and traffic characteristics, which allows to enrich the attribute set of road networks with visual features of satellite imagery and digital surface models. We show that both, the segmentation and the integration of vision data can increase performance on a road type classification task, and we achieve state-of-the-art performance on the OSM+DiDi Chuxing dataset on Chengdu, China.