Learning to integrate vision data into road network data

TL;DR

This paper introduces a method to enhance road network representations for autonomous vehicles by integrating satellite imagery and digital surface models into graph neural network embeddings, leading to improved classification accuracy.

Contribution

The work presents a novel approach to incorporate remote sensing vision data into road network graphs, including a segmentation technique based on spatio-temporal features, achieving state-of-the-art results.

Findings

Improved road type classification performance.

Effective integration of satellite imagery into graph neural networks.

State-of-the-art results on Chengdu dataset.

Abstract

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.