GeoAI - Changing spatial information through artificial intelligence; [GeoAI – Veränderung räumlicher Informationen durch künstliche Intelligenz]

Abstract

This article examines GeoAI and its influence on the way spatial information is collected, processed, analyzed and interpreted. The combination of geospatial technologies with artificial intelligence creates new possibilities for working with large and complex spatial datasets. Traditional GIS workflows often depend on predefined methods and substantial manual effort, while AI can assist with classification, pattern recognition, prediction and automated extraction of information. The article considers how these capabilities can change the production and use of spatial information. Remote sensing imagery, point clouds, maps and other geospatial datasets can provide large amounts of information, but extracting useful knowledge from them can be time-consuming. AI-based methods can help identify relevant patterns and support faster processing. At the same time, the quality of results depends on the data used for training and validation, as well as on the suitability of the chosen method for a particular spatial problem. The article therefore presents GeoAI as an extension of existing geospatial workflows rather than a replacement for geographic expertise. Human knowledge remains important when results have to be interpreted, checked and translated into decisions. Issues such as transparency, data quality, reproducibility and responsible use also become increasingly relevant as AI is integrated into spatial applications. Overall, the article highlights the growing connection between artificial intelligence and geoinformation and shows how this combination can support new forms of spatial analysis, automation and decision-making. This creates opportunities for more efficient geospatial workflows, especially where organizations need to process imagery and other spatial information at a scale that would be difficult to manage manually.