Learning Spiral delivers high-precision annotation services for autonomous vehicle development, transforming raw sensor data into structured, machine-readable datasets. Our expert-driven workflows support object detection, lane segmentation, and scene understanding across diverse driving environments. With rigorous quality control and scalable pipelines, we help AV and ADAS teams train safer, more reliable perception models faster.
AV Annotation
Camera
Our camera annotation services turn raw 2D image and video data into accurately labeled datasets for computer vision models. From bounding boxes and polygons to semantic segmentation, we capture vehicles, pedestrians, traffic signs, and lane markings with precision. This high-quality visual data strengthens object recognition, scene understanding, and real-time detection for autonomous systems.
LiDAR
Learning Spiral provides expert LiDAR point cloud annotation, converting raw 3D scans into structured, high-accuracy training data. Using 3D cuboids, semantic segmentation, and object tracking, we help capture precise spatial geometry, distances, and boundaries. This enables robust obstacle detection, localization, and mapping capabilities essential for autonomous vehicles and robotics applications.
Radar datasets
Our radar data annotation services label range, velocity, and object detection data to support robust autonomous driving perception, especially in low-visibility or adverse weather conditions. By accurately annotating radar signals and integrating them with camera and LiDAR data, we help build resilient, weather-independent sensor fusion models for safer, more reliable autonomous navigation.