GeoAI

The rise of artificial intelligence -based solutions is something one cannot have missed in the past few years. As always with technological breakthroughs, there is a lot of hype, but also a lot of very useful things – and something that will change, if not revolutionize, our entire industry.

Amidst all this noise, we specialize in applying artificial intelligence (AI) solutions to geospatial technologies and data. We always design and identify the best platforms, tools and methods for GeoAI implementations, depending on the use case.

In the geospatial branch, artificial intelligence means much more than just language models and generative prompts and agents – and has been alongside spatial data for decades, for example:

  • Machine vision, object recognition and semantic segmentation provide tools for digitizing analog data or automatically interpreting satellite, aerial and video data.
  • Operations research and optimization algorithms enable rapid calculation of scenarios, for example for location selection and optimization.
  • Using natural language processing (NLP), we are able to provide our customers with methods for semantically interpreting, classifying and analyzing sentiment and structure from qualitative data.
  • Generative artificial intelligence can be used to create the final touch and produce quick and concise summaries from even large quantities of data.

Our GeoAI consulting service, which combines location data and artificial intelligence, connects location analytics and advanced artificial intelligence methods, offering organizations the opportunity to utilize location data in a new, more impactful way.

Our services support the organization’s knowledge management, automation and predictive analytics regardless of industry. The core areas of our GeoAI services are:

  • Spatial data processing and enrichment: Combining, normalizing and pre-processing location data from different sources and other linked data for machine learning needs.
  • Artificial intelligence-based spatial data analysis: Applying deep learning and machine learning methods to geospatial data, such as satellite and aerial images, spatial data registers and sensor data.
  • Predictive models and optimization: Modeling and predicting phenomena using spatial data (e.g. land use changes, risk areas, development needs).
  • Visualization and interactive applications: Intuitive, user-oriented views and user interfaces that enable the use of results in decision-making.
  • Integration into the organization’s architecture: Connecting GeoAI solutions to existing information systems, cloud services, and analytics platforms as production-ready implementations.