MapAnything: Mapping Urban Assets using Single Street-View Images

To maintain an overview of urban conditions, city administrations manage databases of objects like traffic signs and trees, complete with their geocoordinates. Incidents such as graffiti or road damage are also relevant. As digitization increases, so does the need for more data and up-to-date databases, requiring significant manual effort. This paper introduces MapAnything, a module that automatically determines the geocoordinates of objects using individual images. Utilizing advanced Metric Depth Estimation models, MapAnything calculates geocoordinates based on the object's distance from the camera, geometric principles, and camera specifications. We detail and validate the module, providing recommendations for automating urban object and incident mapping. Our evaluation measures the accuracy of estimated distances against LiDAR point clouds in urban environments, analyzing performance across distance intervals and semantic areas like roads and vegetation. The module's effectiveness is demonstrated through practical use cases involving traffic signs and road damage.

Enhancing Roadway Safety: LiDAR-based Tree Clearance Analysis

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.