AI in Space Industry

Graphic representing AI in the Space Industry

These days, artificial intelligence assumes a focal part in the innovative world and our lives more generally. The developments acquired lately ensure the goal of perplexing issues in unthinkable times.

Artificial intelligence in space exploration is a key piece of the IT system that permits present-day space organizations to develop.

This information should be accessible almost immediately and refreshed continuously by rockets and space stations, which are many kilometers. In summary, the safety of spacecraft and their passengers are guaranteed thanks to extremely powerful machines combined with the latest storage technologies. The demand for technologies expands and information should be focused on and set at the core of plan and advancement.

Most common AI Use Cases

Graphic representing AI in the Space Industry

Satellite Object Detection

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    Handling of satellite information, while utilizing ai in space, helps Earth perception satellites produce a vast amount of information that should be appropriately handled. This information could be retrieved by ground stations and should be gathered and dissected utilizing proper calculations. In this sense, computerized reasoning can ensure the chance of breaking down data in an itemized and proficient manner. For instance, one study can present heat produced in urban areas, other can couple meteorological information with satellite pictures to infer assessments of wind properties, while third can create monitoring systems for the sake of the satellites themselves.

Project References

Artificial Intelligence Drone computer vision

How Artificial Intelligence can help with Image detection from Satellites AI in Satellite Object Detection

Group 1277


Computer Vision


AI Superior social responsibility program

Potential industries

Urban Development, Real Estate, Logistics, and Transportation, Finance


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Our Project Approach

The AI ​​project lifecycle has been adopted from an existing standard used in software development. Also, the approach takes into account the scientific challenges inherent in machine learning projects involving software development processes. The approach aims to ensure the quality of development. Each phase has its own goals and quality assurance criteria that must be met before the next stage can be initiated.