RADAR Data Annotation for Robust AI Models

Our expert annotators identify and classify objects precisely within sparse radar point clouds, distinguishing various objects from clutter and noise. Classification is performed against your AI model's exact taxonomy, so labels map directly into your AI model. It enables real-time recognition and tracking, enhancing safety, navigation, and autonomous systems. Precise bounding box annotations contribute to improved situational awareness and collision avoidance. Domain-trained annotators and a multi-stage quality check reduce false-positive clutter in your training data, enabling us to deliver with precision and speed consistently.

We label the precise range and angle of each detected object relative to the sensor, giving your AI model the spatial understanding it needs. Range and angle estimation involves determining the distance and orientation of detected objects. By analyzing the time delay of reflected waves, radar systems calculate object range, while angle estimation determines object position relative to the radar. These measurements enable tracking, navigation, and collision avoidance in various applications.

Our trained team labels Doppler shift signatures to capture the relative velocity of moving objects. It involves analyzing the frequency shift of the reflected waves to calculate the relative motion between the radar sensor and the detected objects. This is critical for distinguishing a moving hazard from a stationary object, particularly in low-visibility conditions where camera and LiDAR performance degrades. This information is crucial for applications such as traffic monitoring, weather forecasting, and aerospace systems.

We annotate static and semi-static environmental features captured in radar point clouds, including road boundaries, barriers, fixed infrastructure, buildings, vegetation, and terrain, to help build a persistent map layer that complements your dynamic object detection. This is typically done by analyzing the radar reflections and correlating them with known features. The annotated data is used to create accurate and detailed maps for navigation systems, urban planning, and simulations.

We filter the unwanted signals or noise from radar data. Signal processing techniques, including background estimation, clutter modeling, and adaptive filtering, are employed to suppress clutter and enhance target detection and tracking. This improves the accuracy and performance of radar-based applications by focusing on desired object signals.

Synthetic Aperture Radar (SAR) is a radar imaging technique that creates high-resolution radar images of the Earth's surface. SAR annotation identifies and labels objects, terrain features, and structural details within radar imagery, distinguishing land cover types, built infrastructure, and surface changes. It supports applications including remote sensing, land surveying, and environmental monitoring, providing the labelled foundation needed for mapping and change-detection analysis.