Transportation Data Annotation & Computer Vision Solutions

Revolutionize transportation with computer vision for smarter, safer, and more efficient systems.
Collage of urban street scenes with multiple vehicles, pedestrians, bicycles, and dogs, each labeled with bounding boxes and tags including car, person, bicycle, and dog.

Data Annotation for Transportation

Enhance transportation systems with precise data annotation, enabling smarter navigation, traffic management, and operational efficiency.
3D LiDAR point cloud visualization showing a blue autonomous car detecting multiple human figures and objects in its surroundings enclosed in colored bounding boxes.

3D Lidar

3D LiDAR annotation provides precise spatial information by labeling point cloud data, enabling accurate object detection, classification, and tracking for applications such as autonomous vehicles, robotics, and smart city systems. This advanced annotation type captures depth, shape, and distance, essential for building robust AI models.
Two large trucks followed by several cars driving on a multi-lane highway with green 3D bounding boxes around each vehicle.

3D Cuboid

3D Cuboid annotation involves creating three-dimensional bounding boxes around objects, capturing their position, orientation, and size in a 3D space. This technique is essential for training AI models in applications like autonomous driving, robotics, and augmented reality, enabling accurate object detection and spatial understanding.
Heavy traffic jam on a multi-lane road with cars, motorcycles, buses, and trucks detected and labeled with colored boxes.

Predict and Mitigate Real-Time Traffic Issues

Enable AI systems to predict and mitigate real-time traffic issues through advanced data annotation. By accurately labeling vehicles, road signs, lane markings, and traffic patterns, this annotation type empowers smarter navigation, congestion management, and safer transportation solutions.
Rear view of a white Peugeot RCZ car parked on cobblestone, with license plate number 6910 TE-7 and country code BY.

License Plate Reading

Promote your services and let potential customers purchase your products in a highly-structured mode.
Urban street scene with multiple 'Do Not Enter' signs, no bicycles allowed, and no pedestrian crossing signs on a city street with traffic and tall buildings.

Road Signs Reading

Road Sign Reading annotation involves labeling and classifying various road signs in images or video data. This annotation type is essential for training AI models in autonomous vehicles, traffic management, and navigation systems, ensuring accurate sign detection and improved decision-making on the road.
Pedestrians crossing a street at a crosswalk, each person highlighted with green detection boxes.

Monitor Pedestrians and Foot Traffic

Monitor Pedestrians and Foot Traffic annotation involves accurately labeling and tracking people in various environments. This annotation type is crucial for applications such as smart city planning, autonomous vehicles, crowd management, and safety analytics, enabling intelligent systems to understand and respond to human movement.
Close-up of damaged asphalt road surface showing a pothole with exposed underlying layer.

Monitor Road and Infrastructure Conditions

Monitor Road and Infrastructure Conditions annotation involves labeling and analyzing road surfaces, infrastructure elements, and environmental factors. This annotation type is essential for maintaining road safety, optimizing infrastructure management, and supporting autonomous vehicle navigation with accurate data insights.

Save Both Time and Money!

Why AI Teams Choose Unitlab

One platform to manage, annotate, and curate training data across every modality, helping teams move faster while staying efficient at scale.

15X

Faster Data Annotation
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Auto Data Collect
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Auto Data Labeling
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Auto Model Validation
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Auto QA
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Auto Model-In-the-Loop

60%

Free Up AI Engineer’s Time
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Dataset Curation
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Dataset Version Control
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Dataset QA
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AI Model Integration
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AI Model Validation

5X

Save AI Development Cost
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5X saving in Data Collection
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10X saving in Data Labeling
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5X saving in Dataset Curation
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5X saving in AI Development
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5X saving in Model Validation
15X
Faster Data Annotation
60%
Free Up AI Engineer’s Time
5X
Save AI Development Cost

Annotation types for Transportation

Explore diverse annotation types for transportation, including vehicle detection, lane marking, traffic sign recognition, and route mapping to power advanced AI solutions.
Busy multilane city street with numerous cars, a person riding a bicycle, and several pedestrians near the crosswalk.

Bounding Box for Object Detection

Self-driving vehicles depend on their ability to perceive their environment for safe navigation. Experts play a key role by annotating bounding boxes, helping the vehicle's computer vision system accurately assess the distance and size of objects on the road and along the sides.

Segmentation for Object Classification

Semantic segmentation enhances object detection in autonomous vehicles by grouping multiple objects of the same category as a single unit. This process involves classifying every pixel in an image based on its semantic meaning, such as identifying all cars or all people within the scene.

Street scene with multiple color-segmented vehicles including SUVs and cars, green road, red buses, and trees along city buildings.
Rear view of a white Honda car driving on a highway with a green line and dots graphic overlay following the road.

Polyline Annotation for Lane Detection

Autonomous vehicles rely on environmental perception to navigate safely. Specialists contribute by marking bounding boxes, enabling the vehicle's computer vision system to accurately determine the size and distance of objects on and around the road.

Polygon for Shapes

Access expert assistance to accurately trace polygons around irregularly shaped objects. This enables computer vision systems in autonomous vehicles to detect all visible objects on the road, such as motorcycles, bicycles, cars, and animals, ensuring safe driving by preventing collisions.

Small green hatchback car parked partially on a paved shoulder beside a highway guardrail.

Frequently Answered Questions

Is Unitlab AI a free data annotation tool?

Yes. You can use Unitlab AI for free, no credit card required. The free plan is great for getting started and testing workflows. If you need more scale, advanced features, or custom setups, paid plans are available.

Can I change my plan after creating an account?

Yep. Start free and upgrade anytime. Most teams begin on the free plan and move to a paid plan once their datasets or team size grow.

What types of data can I annotate with Unitlab AI?

Unitlab AI supports multimodal data annotation, including image, video, audio, text, and DICOM. You can manage and annotate different data types in one platform using the same workflows.

Does Unitlab support AI-assisted and auto-annotation?

Yes. Unitlab AI includes built-in AI models to speed up annotation with auto-labeling, tracking, and segmentation. You can review, edit, and validate everything to keep quality high.

Can I use my own AI models with Unitlab AI?

Absolutely. Unitlab supports Bring Your Own Model (BYO) workflows. You can plug in your own models, combine them with Unitlab’s built-in models, and run multiple models in a single annotation workflow.

How does pricing for data labeling services work?

Pricing depends on the type of annotation, dataset size, number of classes, and complexity. Labeling services start from $0.02 per image, with custom pricing available for multimodal and large-scale projects.

Who has access to my data?

You do. With on-premises deployments, all data stays inside your own infrastructure and Unitlab has no access to it.
 If you use Unitlab’s hosted platform, your data is encrypted and isolated, and is not visible to Unitlab staff. Access is strictly controlled and limited to your team based on roles and permissions.

Does Unitlab support team collaboration and review workflows?

It does. Unitlab is built for teams, with support for multiple annotators, reviewers, approval steps, and full annotation history so nothing gets lost.

Can Unitlab handle large datasets and production workloads?

Yes. Unitlab is designed to scale, from small experiments to production-level data annotation. Teams use it to manage large datasets, complex workflows, and long-running annotation projects.

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