  In the efforts for safer roads, ensuring adequate vertical clearance above
roadways is of great importance. Frequently, trees or other vegetation is
growing above the roads, blocking the sight of traffic signs and lights and
posing danger to traffic participants. Accurately estimating this space from
simple images proves challenging due to a lack of depth information. This is
where LiDAR technology comes into play, a laser scanning sensor that reveals a
three-dimensional perspective. Thus far, LiDAR point clouds at the street level
have mainly been used for applications in the field of autonomous driving.
These scans, however, also open up possibilities in urban management. In this
paper, we present a new point cloud algorithm that can automatically detect
those parts of the trees that grow over the street and need to be trimmed. Our
system uses semantic segmentation to filter relevant points and downstream
processing steps to create the required volume to be kept clear above the road.
Challenges include obscured stretches of road, the noisy unstructured nature of
LiDAR point clouds, and the assessment of the road shape. The identified points
of non-compliant trees can be projected from the point cloud onto images,
providing municipalities with a visual aid for dealing with such occurrences.
By automating this process, municipalities can address potential road space
constraints, enhancing safety for all. They may also save valuable time by
carrying out the inspections more systematically. Our open-source code gives
communities inspiration on how to automate the process themselves.
