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Interested in scenario driven development? Reach out to us via to extract scenarios today!

understand.ai provides high-quality training and validation data to enable mobility companies to develop with confidence computer vision and machine learning models that reliably and safely power autonomous vehicles.

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Achieve the most reliable safety standard and the best results by training and validating your algorithms with accurate annotations made with German precision.

Fast and flexible

Our system is fast and flexible in its approach. Manage projects with more than 100 million annotated images up to 5 times faster than conventional approaches.

Compliant and secure
data ownership

Own your data throughout the annotation process. Our processing is compliant to the newest GDPR regulations via services like advanced data anonymization and de-personalization. For additional privacy and security, EU-based data centers are available on request.

Team of technical

Profit from our creative solutions, which are adapted and tailored to your project with its unique requirements and success criteria. We will make sure to meet your individual timeline.

Known from

Our portfolio covers the diversity of all regular raw data formats, project requirements and annotation types. This includes 2D images, semantic enhancements, video annotations and LiDAR.


Pixelwise Segmentations

Since the world is not made out of boxes, we are also offering a more precise method to annotate your data - semantic segmentation.

Depending on the raw data, bounding boxes can contain noise in the form of background and occlusions. This is tackled with semantic segmentation, where each pixel assigned to the class of your selected objects will be annotated. It is therefore the closest to a true representation of reality in 2D space, regarding class assignments. It also is more versatile, since it is very easy to distinguish between objects, e.g., road, lanes and curbs and it is possible to annotate classes that are not instantiable.

Bounding Box

Bounding Boxes

The annotation type with the most scientific research and most commonly used is bounding boxes. They are easy to apply to machine learning models and faster to annotate in comparison to other annotation types.

Unlike segmentation, bounding boxes may also contain invisible parts of the classified object by approximating occlusions. Due to the inherent instance-awareness of bounding boxes, your algorithms will get a better understanding of the concept of specific objects.