Understand the role of LiDAR in self-driving cars. Learn how this technology enhances autonomous driving systems and ensures safer roads.
CloudFactory Blog
Agriculture is ripe for autonomous technology powered by computer vision. Learn how automation and robotics are impacting farming operations.
This blog post is about how AI-enabled drones use object detection and collision avoidance computer vision technology for drone delivery at scale.
Computer vision models for autonomous checkout require a lot of high-quality labeled training data. Learn how to scale and accelerate data annotation.
Explore where ADS and ADAS technologies fit within the levels of driving automation, and learn how they enhance safety and autonomy in modern cars.
Shoppers count on a frictionless retail experience, especially during checkout. Here’s how AI and technology improve the customer experience in stores.
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.
These four use cases examine why using drones to collect data makes drone inspections safer, more accurate, and more efficient than manual inspections.
Recap of our chat with V7 about medical AI data annotation while creating a dataset of COVID-19 X-rays for clinical use and diagnostic AI development.
Learn about the levels of drone autonomy and how advancements in AI are transforming drone operations across various industries.
Drone autonomy fueled with high-quality data will be the key to scaling drone operations. Read more valuable insights from the drone team at CloudFactory.
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.
Sentiment analysis can turn the abundance of online information into actionable insights, but machines can’t do everything by themselves.
