Powering vision AI applications for
Drones &
Aerial Imagery
Weaving human and machine intelligence to help deep learning teams reach production at scale
Enriching AI capabilities in the drones and aerial imagery space
Visual data produced by drones, and remote sensing systems in general, is characterized by high complexity. It often involves input from multiple sensors, which need to be synchronized for interpretation; it may include faraway objects and substances, sometimes visible only via LiDAR or thermal displays; it might also requires expert knowledge, both during data analysis and during on-site implementations.
To answer such demands, Dataloop’s platform integrates sensor fusion and data processing capabilities with automatic active learning, combined with expert human-in-the-loop workforce for data validation. This means you can use your models to pre-annotate and pre-process incoming data, then have expert annotators audit and fix the data output, instead of labeling from scratch – resulting in continuously improved model accuracy, while saving significant resources and time.
Dataloop's real-world applications for UAVs
Select use cases that Dataloop advances
Dataloop’s Use-cases
Enterprise
Power collaborations across the organization
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data Annotation
High quality annotations made fast and easy
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Data operations
A single source of truth for your ML data
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deep learning
Build, develop and scale your pipelines
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Powering vision AI teams across the globe
Helping companies create AI-driven products, from development to large-scale production
“Dataloop has enabled us to build our production environment with data pipelines and annotation workflows, helping us grow our product. Their ML experts work with us closely to meet our needs.”
“Data accuracy is critical to the development of our autonomous systems. Dataloop provides our team with a powerful and intuitive platform that allows us to create top quality and accurate datasets."
Elbit Systems
“The team at Dataloop provide a powerful platform with a suite of tools enabling us to successfully test our algorithms and imporove our ADAS and autonomous driving features.”
