  Lane graph estimation is an essential and highly challenging task in
automated driving and HD map learning. Existing methods using either onboard or
aerial imagery struggle with complex lane topologies, out-of-distribution
scenarios, or significant occlusions in the image space. Moreover, merging
overlapping lane graphs to obtain consistent large-scale graphs remains
difficult. To overcome these challenges, we propose a novel bottom-up approach
to lane graph estimation from aerial imagery that aggregates multiple
overlapping graphs into a single consistent graph. Due to its modular design,
our method allows us to address two complementary tasks: predicting
ego-respective successor lane graphs from arbitrary vehicle positions using a
graph neural network and aggregating these predictions into a consistent global
lane graph. Extensive experiments on a large-scale lane graph dataset
demonstrate that our approach yields highly accurate lane graphs, even in
regions with severe occlusions. The presented approach to graph aggregation
proves to eliminate inconsistent predictions while increasing the overall graph
quality. We make our large-scale urban lane graph dataset and code publicly
available at http://urbanlanegraph.cs.uni-freiburg.de.
