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Analyse efficiently, save downlinking costs

Superhuman Vision accurately detects critical targets and removes unusable data, before ever downlinking.

Space

Lightweight, on-premise solutions

With models as small as 5mb, our power efficient solutions are optimized for low-latency, low-data scenarios where every ounce of hardware is critical.

Trainable on limited data

Uses 5-10 images to learn new, specialized use cases

Simple installation

Easily integrates with existing hardware, supports SAR

Super secure

All sensitive data remains securely with clients.

Hear it from our customers

Space

Make your satellites smarter with a lightweight, next-gen computer vision SDK.

Space
Asset Management
Coming Soon

Visual data management made easy

Asset Management
Coming Soon

Visual data management made easy

Stock Photography
Coming Soon

Easily identify relevant visuals for purchase

Stock Photography
Coming Soon

Easily identify relevant visuals for purchase

Press & Broadcasting
Coming Soon

Empower journalists with tagged visual archives

Press & Broadcasting
Coming Soon

Empower journalists with tagged visual archives

Video Services
Coming Soon

Moderate & manage video content with ease

Video Services
Coming Soon

Moderate & manage video content with ease

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Metadata Mastery

FREE training from the experts at Mobius Labs

Short videos  |  Webinars  |  Exclusive Previews  
Dominic Rüfenacht - Head of Science | Co-founder bei Mobius Labs GmbH
Mobius Labs GmbH is receiving additional funding by the ProFIT program of the Investment Bank of Berlin. The goal of the ProFIT project “Superhuman Vision 2.0 for every application- no code, customizable, on- premise  AI solutions ” is to revolutionize the work with technical images. (f.e.) This project is co-financed by the European Fund for Regional Development (EFRE).

In the ProFIT project, we are exploring models that can recognize various objects and keywords in images and can also detect and segment these objects into specific pixel locations. Furthermore, we are investigating the application of ML algorithms on edge devices, including satellites, mobile phones, and tablets. Additionally, we will explore the combination of multiple modalities, such as audio embedded in videos and the language extracted from that audio.