Learning Spiral provides comprehensive road scene understanding through detailed image and video annotation, helping autonomous systems interpret complex driving environments. Our labeling covers roads, intersections, traffic signs, signals, and obstacles, enabling AI models to accurately perceive surroundings, predict movement, and make safer real-time decisions across diverse urban, highway, and rural driving scenarios.
Road Scene Understanding
Lane Labeling
Precise lane annotation is essential for safe autonomous navigation. Learning Spiral offers accurate lane marking, lane boundary, and lane divider labeling across varied road conditions, weather, and lighting. Our high-quality datasets help train AI models for lane detection, lane-keeping assist, and path planning, ensuring reliable performance for autonomous vehicles and ADAS systems.
Vehicle Labeling
Learning Spiral delivers precise vehicle annotation using bounding boxes, cuboids, and segmentation across cars, trucks, bikes, and other road users. Our labeling captures vehicle type, position, orientation, and movement across diverse traffic scenarios, enabling accurate object detection, classification, and tracking for autonomous driving, fleet monitoring, and advanced driver-assistance systems.
Pedestrian Labeling
Accurate pedestrian annotation is critical for road safety. Learning Spiral provides precise labeling of pedestrians, cyclists, and other vulnerable road users across varied environments, poses, and occlusion levels. Our high-quality datasets support robust pedestrian detection and tracking models, helping autonomous vehicles and ADAS systems respond safely and reliably in real-world conditions.