In AI datasets, LiDAR data is annotated to help models understand the 3D world. Typical annotation tasks include:

• 3D Bounding Boxes – Drawing boxes around vehicles, pedestrians, trees, or other objects in 3D space.


• Segmentation – Labeling each LiDAR point according to object type (e.g., road, car, building).


• Sensor Fusion Annotation – Merging LiDAR data with camera images for more accurate labeling.


• Depth & Distance Mapping – Annotating the relative distance of each object from the sensor.

Radar data annotation involves identifying and labeling objects based on radar reflections and motion patterns:

• Object Detection & Tracking – Annotating moving or stationary targets (vehicles, people, obstacles).


• Velocity Annotation – Labeling speed or direction of moving objects.


• Sensor Fusion – Combining radar with camera or LiDAR to improve recognition in low-visibility conditions.


• Region-of-Interest Marking – Highlighting radar reflections corresponding to real-world objects.

• Autonomous Vehicles – Used together for object detection, obstacle avoidance, and environment mapping; LiDAR provides 3D scene details while Radar measures object distance and speed.


• Robotics & Drones – Enable navigation, collision prevention, and spatial awareness in dynamic or low-visibility environments.


• Smart Cities & Infrastructure – Support 3D mapping, traffic monitoring, and infrastructure inspection.


• Surveillance & Security – Detect and track people, vehicles, or intrusions in all weather and lighting conditions.


• Aviation & Maritime Systems – Assist in terrain mapping, collision avoidance, and target tracking.


• Environmental Monitoring – Used for topographic mapping, forest analysis, flood assessment, and disaster management.


• Agriculture – Support precision farming by analyzing terrain, crop density, and moisture levels.


• Industrial Automation – Enable machine safety, perimeter detection, and intelligent process monitoring.