From Raw Data to Real-World Context

Capture, enrich, and visualize spatial data with precision—powering smarter GIS models and analysis.

Annotation and Labelling

Annotation and labeling are fundamental processes in creating High-definition (HD) maps, ensuring accurate object recognition, scene understanding, and enhanced safety for autonomous vehicles.

Precise labelling and annotation play a crucial role in developing reliable HD maps. Advanced AI-powered annotation tools, combined with human expertise, ensure comprehensive data quality. Continuous validation, refinement, and integration of labelled data contribute to safer and more efficient autonomous driving systems. As the industry progresses, automated labelling solutions and collaborative annotation platforms will further streamline the HD map generation process, supporting the evolution of intelligent transportation systems.

Annotation and Labelling

Annotation and Labeling are fundamental processes in creating High-definition (HD) maps, ensuring accurate object recognition, scene understanding, and enhanced safety for autonomous vehicles.

Precise labeling and annotation play a crucial role in developing reliable HD maps. Advanced AI-powered annotation tools, combined with human expertise, ensure comprehensive data quality. Continuous validation, refinement, and integration of labeled data contribute to safer and more efficient autonomous driving systems. As the industry progresses, automated labeling solutions and collaborative annotation platforms will further streamline the HD map generation process, supporting the evolution of intelligent transportation systems.

Our Capabilities Include

Multi-Class Object Labeling

Accurately identify and classify a wide range of objects—including road signs, lane markings, pedestrians, vehicles, and surrounding infrastructure—across both 2D and 3D spatial formats.

Sensor Fusion Support

Integrate and annotate data from LiDAR, radar, camera, and GPS sources to create comprehensive, multi-modal training datasets—enabling more accurate perception models and robust real-world simulations..

Semantic Segmentation

Deliver fine-grained, pixel-level labeling of road features, boundaries, and lane markings to train high-accuracy AI vision models for advanced scene interpretation and navigation.

Temporal Annotation

Apply frame-by-frame annotations with temporal awareness to track object motion and support accurate behavioral prediction in complex, dynamic environments.

Automated Pre-Labeling Pipelines

Enhance efficiency using machine learning-powered tools that auto-generate label suggestions—streamlined by automation and validated by expert QA teams for accuracy.

Continuous Quality Validation

Implement iterative review cycles and structured feedback mechanisms to maintain the accuracy and reliability of labeled data throughout its lifecycle.