LiDAR Data Annotation

LiDAR data annotation involves labeling three-dimensional point cloud data generated by LiDAR (Light Detection and Ranging) sensors. These annotations enable machine learning models to interpret spatial features with depth, making LiDAR essential in applications where precise elevation and structural data are needed.

LiDAR sensors emit laser pulses and measure their return time to build a 3D map of the surrounding environment. The resulting point clouds consist of millions of data points with x, y, z coordinates and often additional attributes like reflectance or intensity.

Types of LiDAR annotation:

• Bounding boxes and cuboids for object detection (e.g., trees, vehicles, buildings)
• Point-wise classification to label ground, vegetation, structures, etc.
• Semantic and instance segmentation for detailed terrain or infrastructure mapping
  

Key applications of LiDAR annotation:

• Autonomous navigation: Terrain modeling, obstacle detection
• Forestry: Tree counting, canopy height, biomass estimation
• Civil engineering: Infrastructure modeling, road surface analysis
• Flood modeling: Elevation mapping and drainage analysis
  

Annotation of LiDAR data is often performed using 3D visualization tools such as Potree, CloudCompare, or specialized platforms like Supervisely and Scale AI. High-quality labeled LiDAR datasets are critical for building spatially aware AI models that operate in complex 3D environments.