GEO AI & Mobility
Geo-AI and spatial data analysis services.
GEOAI & Mobility Unlock new potential in geodata with state-of-the-art AI solutions Our AI experts specialise in geodata and support you in delivering your individual projects in the geospatial domain. With deep technical knowledge and the latest technology, we build solutions tailored to your requirements. We organise our expertise into three core areas: AI on 3D data AI on 2D data Mobility and geodata in Touch Why Supper & Supper for your GeoAI project? Deep industry knowledge Our AI experts bring solid expertise and many years of experience in the geodata field. We combine this know-how with cutting-edge AI capability. Specialised in geodata Our team is highly specialised in 2D, 3D and mobility data. With years of project experience, we either run projects end to end or support you as an outsourced workbench. Strategic consulting Drawing on projects with large corporations, SMEs and the public sector, we deliver against very different requirements efficiently. Project management We guarantee clear communication, regular updates and full transparency, from planning through to delivery. What is GeoAI? Geospatial Artificial Intelligence (GeoAI) combines artificial intelligence with geodata to analyse spatial relationships and uncover new insights. The focus is on processing and interpreting geographic information such as 3D models, maps and satellite data. AI methods can, for example, detect complex patterns in mobility and traffic data, model environmental change, or analyse 2D and 3D data more efficiently. With GeoAI, geographic data is not just visualised but intelligently interpreted, creating value in areas such as climate protection, infrastructure management and mobility. Organisations benefit from more efficient processes, better decision-making foundations and innovative approaches. Where does AI add value in the geospatial field? Using artificial intelligence in the geospatial field opens up entirely new ways to use geodata efficiently and precisely. AI can analyse large volumes of 2D and 3D data quickly, recognise complex patterns and generate forecasts that would be impossible by hand. This speeds up decision-making and puts it on a firmer footing. The value shows clearly in traffic planning, environmental monitoring and urban development: optimised traffic flows, early risk detection and sustainable use of resources. Companies and organisations benefit from automated analysis, better accuracy and innovative approaches that help them meet challenges efficiently. With AI in the geospatial field, data is not only visualised but translated into practical, actionable insight, a decisive advantage in a data-driven world. AI on 3D data What does AI on 3D data mean? By AI on 3D data we mean the processing and analysis of complex three-dimensional datasets such as point clouds, 3D models and other spatial information. This data offers valuable insight that artificial intelligence can put to use efficiently and precisely. What do we do here? Our team specialises in analysing point clouds. With our platform Pointly.ai we apply state-of-the-art AI technology to classify, structure and evaluate these complex datasets. This enables fast, scalable processing tailored to a wide range of industries and applications. Example use cases:
- Urban planning: Detailed analysis and automatic classification of point clouds to categorise and optimise urban structures and spot potential planning errors early.
- Infrastructure management: Mapping and analysis of roads, rail infrastructure and power lines
- Condition assessment: Monitoring and maintenance of structures such as bridges and tunnels.
- Agriculture: Using 3D data for precise surveying and classification of agricultural land.
- Forestry: Classification of tree species and monitoring of woodland areas.
- Real estate: Support in creating highly precise 3D models for construction projects or property valuations.
- Traffic management: Identifying traffic flows and bottlenecks in urban areas.
- Urban planning: Optimising transport routes, land use and traffic-sign cadastres.
- Climate research: Analysing satellite data to monitor environmental change.
- Agriculture: Detecting cultivated areas and monitoring soil health.
- Disaster management: Early detection and risk assessment for natural disasters such as flooding.
- Infrastructure management: Precise monitoring and condition assessment of structures such as bridges and tunnels.
- Renewable energy planning: Optimising sites for solar farms and wind turbines.
- Vehicle sensor data: Using real-time data from vehicles for traffic monitoring and control.
- Smart city integration: Linking mobility data with other urban data sources for smarter traffic planning.
- GPS data analysis: Recognising movement patterns and optimising routes.
- Fleet management: Improving efficiency and reducing empty mileage in logistics and transport.
- Traffic-flow optimisation: Analysing data to reduce congestion and optimise traffic-light timing.
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