Vast data volumes handled
Algorithms process the stream of data received by ground stations in a detailed and efficient way, instead of relying on manual review.
Industries · Space and Earth observation
AI in Space Industry from satellite data to usable insight
Earth observation satellites produce vast amounts of data that ground stations must collect and analyse quickly. We build deep learning systems that process satellite and hyperspectral imagery in detail, for civil uses such as environmental monitoring, urban analysis and infrastructure inspection.
- Satellite imagery analysed in detail and at scale
- Small objects detected from orbit
- Monitoring of the environment and infrastructure
- 57% of our team hold a PhD
- German company, working under the GDPR
Discuss your project
5.0 18 reviews on Clutch Go Global Awards winner
Trusted by enterprises, scale-ups and non-profits
What you gain
What AI changes in the space industry
Data from orbit is only useful when it can be processed quickly and accurately. That is where AI comes in.
More seen than the human eye
Deep learning uses all channels of hyperspectral imagery, so it can detect substances such as oil spills or methane leaks that are invisible in red, green and blue.
Monitoring of the satellites themselves
AI can support monitoring systems for satellites, helping operators keep track of spacecraft condition.
What we deliver
AI solutions for space and Earth observation
Civil applications of AI on satellite and remote sensing data.
- 01
Satellite object detection
Deep learning that recognises objects as small as 10 x 8 pixels, such as buildings, cars, trains, roads, highways and railways.
- 02
Environmental and pollution monitoring
Detection of coastal and marine plastic debris, oil spills and methane leaks, with severity analysis.
- 03
Urban heat analysis
Studies of the heat produced in urban areas based on satellite data.
- 04
Wind estimation from satellite and weather data
Combining meteorological data with satellite images to estimate wind properties.
- 05
Infrastructure and land-use monitoring
Road and railway condition, coverage by sand or snow, parking areas, construction sites and unauthorised building.
- 06
Satellite health monitoring
Monitoring systems built for the satellites themselves.
- 07
Vegetation and land degradation analysis
Long-term analysis of vegetation indices such as NDVI to track land degradation and regeneration.
Not sure which of these you need? Describe the problem and we will recommend the approach.
Discuss your projectCase studies
All case studiesResults we delivered for our clients
Space · Computer Vision
Object detection in hyperspectral satellite imagery
10 x 8 pxsmallest object size recognised
An environmental monitoring solution, developed as part of our social responsibility programme, that detects small objects in hyperspectral satellite imagery with very high accuracy.
Read the case study
Land degradation and conflict prevalence research for an NGO
74% accuracy predicting conflict occurrence 6 months ahead
Data science research with the humanitarian NGO World Vision, analysing satellite vegetation data (NDVI) and historical conflict records to study how land degradation relates to conflict prevalence.
Drone-based litter detection and collection planning
25x faster litter detection, with overall costs halved
A computer vision system that detects litter in drone images across roughly one thousand square kilometres of coastline, with a GIS application for planning and tracking collection.
Map segmentation for urban zone pricing on a real estate platform
5 classes of land use mapped pixel by pixel
A deep learning system that segments city maps, compares them with historical maps and tracks open land over time to support price assessment of urban zones.
Roof detection and segmentation for a solar energy company
Automated roof detection and roof plane segmentation
Deep learning models that detect residential roofs in satellite images, segment them into distinct planes and estimate the area and dimensions of each, so solar panel installations can be planned efficiently.
Why AI Superior
Research-grade AI, delivered like a product company
Many vendors can build a demo. Making AI work reliably on real data, inside real business processes, is the hard part, and it needs a different kind of team.
01
A research-led team
AI Superior was founded in 2019 by AI researchers with published papers and patents, and the whole team works to that research standard.
- PhD57%
- MSc28%
- BSc15%
02
Built to reach production
Every project is de-risked in stages, with a go/no-go decision at each milestone, so budget only goes into approaches that have worked on your data.
4× our proof-of-concept success rate compared with the market average
03
A complete team from day one
Strategy, models and software are designed by one team, so nothing gets lost in hand-overs between vendors.
- Product AI product owner, business analyst, project manager
- Data Data scientists, ML engineers, BI analyst
- Development Software engineers, QA, DevOps
04
Your data stays under control
When data must not leave your organisation, we build on private, self-hosted models instead of third-party AI services.
- GDPR, German company
- Self-hosted LLMs
- Transparent methods
Our team
The experts behind your project
Your project is led by senior data scientists and engineers with research backgrounds, supported by our product and development teams.
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Dr. Sergey Sukhanov Chief Data Scientist PhD in Machine Learning, data scientist since 2013. Publications, patents and 30+ implemented AI use cases; several IEEE awards.
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Dr. Ivan Tankoyeu Chief Data Officer PhD in Computer Science. Recognised by the IEEE GRSS Society, winner of the Global AI Hackathon 2017, Kaggle TOP500 data scientist (2014).
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Sergio Frayle, M.Sc. Lead Data Scientist Master’s in AI, Pattern Recognition and Digital Imaging; 7+ years in image processing and computer vision, with publications.
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Enrique Fernández, M.Sc. Senior ML Expert Master’s in Robotics and Mechatronics; builds and deploys complex AI solutions, particularly in computer vision and NLP.
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Peter Luck, M.Sc. AI Consultant Master’s in Business Engineering and Computer Science; product and project manager for machine-learning products.
Client reviews
5.0 Rated 5.0 by the people we build for
18 verified reviews on Clutch
“AI Superior responded to all of our needs very carefully and considered them in the project.”
Built AI models and a dashboard for a pilot program that detects waste in drone imagery.
“They successfully fulfilled every component of the project and exceeded our expectations.”
Built an AI model that analyzes the roofs of residential and commercial properties, including data collection and labeling. All key deliverables were completed on time.
“They delivered an extra module that we were not expecting but turned out to be very useful.”
Research and custom software development applying generative adversarial networks for a pharmaceutical company with 5,000+ employees.
How we work
A staged AI project life cycle
Every stage ends with a result you can check, and you decide on the next one only after seeing the previous one work. You keep control of scope and budget.
- Estimate before you commitYou see scope and expected results before the build begins.
- Go/no-go after every stageEach stage ends with a result you can check and a decision on the next step.
- Risks reported openlyWe share risks and opportunities as soon as the analysis shows them.
- Go / no-go decision
Discovery
We work through the business problem with your team and define the direction of the solution.
You get: Scope, approach and a high-level estimate of effort and expected results
- Go / no-go decision
Data and feasibility
We get to know your team and data and check whether AI is the right tool for this problem.
You get: A data assessment and a clear feasibility verdict before any build starts
- Go / no-go decision
Proof of concept / MVP
We start small, using the data already available, to test the solution in practice.
You get: Measured results on your own data and a basis for the investment decision
- Go / no-go decision
Integration and scaling
We integrate the solution into your existing systems, fine-tune the models and adjust them where needed.
You get: A solution running inside your processes, compatible with your data and systems
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Evaluation
We review the results with you and make sure they are read correctly.
You get: What the solution delivered and where to improve next
Awards and recognition
Ranked among the top AI companies
Recognised by international business awards and by independent B2B platforms that rank companies on verified client reviews.
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Go Global Awards Winner 2021 · International Trade Council -
Best Data Science & AI Service Provider, Europe 2021 · German Business Awards -
Top Artificial Intelligence Company 2023 · Clutch -
Top Machine Learning Company 2023 · Clutch -
Clutch Champion Fall 2023 · Clutch -
Clutch Global Fall 2023 · Clutch -
Top BI & Big Data Company Germany 2023 · Clutch -
Top IT Services Company Germany 2023 · Clutch -
Top Artificial Intelligence Companies 2023 · TrueFirms -
Top Machine Learning Companies 2021 · Techreviewer -
Most Reviewed IT Services Companies Germany · The Manifest
What can AI detect in satellite images?
Our deep learning approach detects residential and commercial buildings, cars, trains, roads, highways and railways, as well as coastal and marine debris and oil spills. It works on very small objects, down to around 10 x 8 pixels.
Why use hyperspectral imagery?
The human eye only sees red, green and blue. Hyperspectral imagery has many more channels, and models that use all of them can detect substances such as methane leaks or oil spills that are otherwise invisible.
Is your satellite technology proven?
It is based on an award-winning solution recognised by the IEEE society. We applied it in an environmental monitoring project run as part of our social responsibility programme.
Which organisations use this kind of analysis?
Environmental and pollution control, building control authorities, road and railway operators, urban development, logistics, real estate and finance can all use object detection from satellite imagery.
Can satellite images be combined with other data?
Yes. Meteorological data can be coupled with satellite images to estimate wind properties, and long-term vegetation indices such as NDVI can be combined with other records to find correlations and build predictive models.
Start your project
Tell us what you want AI to do for your business
Share a few details and our AI team will take it from there. Here is what happens next:
- We review your request and reply by email.
- A call with an AI expert to understand your problem, data and goals.
- A clear recommendation: the approach we suggest and a high-level estimate.
Prefer to pick a time yourself?
Schedule a call