Training Data for Drones & Aerial Imagery
Images, Videos or 3D Cuboids
Scale generates high quality datasets for all data labeling needs.
How it works
Sophisticated tooling and rigorous QA processes for a wide variety of manufacturing use cases. Simply send requests to our REST API for impeccable training data.
Explore Our Docs"Please label all cars, pedestrians, and cyclists in each frame."
client.createLidarAnnotationTask({
'instruction': 'Please label all cars, pedestrians, and
cyclists in each frame.',
'labels': ['car', 'pedestrian', 'cyclist'],
'meters_per_unit': 2.3,
'max_distance_meters': 30
}, (err, task) => {
// do something with task
});Impeccable Quality
We have built out sophisticated quality assurance processes with in-house specialists to ensure we only produce quality results even at large scales.
Integrated with Machine Learning
We apply machine learning in a multitude of ways to make our workforce more efficient and ensure impeccable quality and accuracy.
Built For Scale
We process millions of images a month and are built to rapidly scale up and down to meet your requirements.
A Comprehensive Set of Tools
Built by machine learning engineers for machine learning engineers, our rich set of tools can handle a wide range of labeling requirements.
Custom Annual Plans and SLAs
Pay on-demand per task or chat with us about an Enterprise Plan.
Contact SalesGuaranteed Task Completion Time
Enterprise-grade SLAs include task completion times and tasks can be rapidly scaled up or down to meet your needs.
24/7 Development Support
Each enterprise customer is paired with a dedicated customer success engineer who will ensure smooth integration and continued service.
Cost Effective
Enterprise engagements include upfront and volume-based discounts, and is the most cost-effective solution for high-quality labels. Plus with Scale, there are no platform fees.
Best-In-Class Quality Choice
Industry leading quality datasets safe-guarded against ever-changing, messy, real-life driving data.
Super Human Accuracy
Our tasks are annotated by trained and qualified workers with additional layers of both human, data and machine learning driven quality control checks.
The resulting accuracy is consistently much higher than what a human or synthetic labeling approach can achieve independently, as measured against seven rigorous quality areas for each annotation.
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