Learning to integrate vision data into road network data
- URL: http://arxiv.org/abs/2112.10624v1
- Date: Mon, 20 Dec 2021 15:38:49 GMT
- Title: Learning to integrate vision data into road network data
- Authors: Oliver Stromann, Alireza Razavi and Michael Felsberg
- Abstract summary: Road networks are the core infrastructure for connected and autonomous vehicles.
We propose to integrate remote sensing vision data into network data for improved embeddings with graph neural networks.
We achieve state-of-the-art performance on the OSM+Di Chuxing dataset on Chengdu, China.
- Score: 14.86655504533083
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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