Summary of Enhancing Roadway Safety: Lidar-based Tree Clearance Analysis, by Miriam Louise Carnot et al.
Enhancing Roadway Safety: LiDAR-based Tree Clearance Analysis
by Miriam Louise Carnot, Eric Peukert, Bogdan Franczyk
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computational Geometry (cs.CG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel algorithm that utilizes LiDAR technology and semantic segmentation to automatically detect and trim tree branches growing over streets, thereby ensuring adequate vertical clearance for safer roads. The algorithm processes LiDAR point clouds at the street level, filtering out irrelevant points and downstream processing steps to create a volume that needs to be kept clear above the road. Challenges include obscured stretches of road, noisy unstructured LiDAR point clouds, and assessing the road shape. The identified points can be projected onto images, providing municipalities with visual aids for addressing potential road space constraints and enhancing safety. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make roads safer by using special cameras that create 3D pictures (LiDAR) to detect when tree branches are growing over streets. This is important because it blocks traffic signs and lights, making driving more dangerous. The algorithm can automatically find the parts of the trees that need to be trimmed, which will save time for municipalities and make roads safer. |
Keywords
» Artificial intelligence » Semantic segmentation
