Data Labeling

CloudFactory is a HIPAA-compliant business associate, committed to safeguarding personal health information for Medical AI and healthcare data projects.

Learn how the levels of driving automation in autonomous vehicles are determined by the presence of ADS with AI technology or ADAS.

GPS free navigation technology enables the safety, scalability, and productivity of drone deliveries in urban areas where satellite signals can be lost.

A panel of drone tech pioneers talks about how AI and computer vision, backed by high-quality data, is shaping the future of drone inspections.

Here are four reasons that doctors shouldn't waste time on tedious data labeling and a solution to accelerate medical AI data training data pipelines.

LiDAR data labeling needs to be precise in autonomous driving as the data gives vehicles a 3-D image to interpret surroundings safely.

This blog post is about how AI-enabled drones use object detection and collision avoidance computer vision technology for drone delivery at scale.

This article discusses how the levels of driving automation are determined by the presence of ADS with AI technology or ADAS.

Geospatial data is fueling innovation in many sectors. Read on if your business needs to scale its data labeling operations while maintaining quality.

Are you considering outsourcing data labeling work while developing medical AI or computer vision solutions? Read these data security tips.

Doctor shortages are impacting healthcare innovators’ ability to label data for computer vision solutions. Outsourcing the work will help.

Computer vision improves patient care, streamlines medical decisions, and lowers costs. Ask these questions before outsourcing healthcare data annotation.

These four use cases examine why using drones to collect data makes drone inspections safer, more accurate, and more efficient than manual inspections.

The demand for drone autonomy is rapidly increasing. See how CloudFactory defines the levels of drone autonomy.

The real-time use cases for geospatial data are practically limitless, but data-labeling on such an enormous scale remains a major challenge.

The nuances of language can be difficult for a machine to understand, hence the need for human input to accelerate testing and ensure quality control.