Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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. 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.

29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio