Intergeo 2019 - DDGI for the second time with its own booth in association with strong partners; [Intergeo 2019 - DDGI zum zweiten mal mit einem eigenen stand in gemeinschaft mit starken partnern]
Keywords:
Geo-IT, Artificial Intelligence, GIS, Machine Learning, Geospatial Data, Digital Transformation, Spatial AnalysisAbstract
This article examines the relationship between Geo-IT and artificial intelligence and considers whether their combination represents the next stage of the digital revolution. Geographic information technologies already support the collection, management, analysis, and visualization of spatial data. Artificial intelligence introduces additional capabilities for recognizing patterns, processing large datasets, and automating analytical tasks. The article discusses how these technologies can complement each other and create new opportunities for geospatial applications. AI can be applied to imagery, point clouds, maps, sensor information, and other forms of geographic data, while Geo-IT provides the spatial framework needed to understand and use the results. The integration of the two fields may support faster analysis, automated classification, predictive models, and improved decision-making. At the same time, successful applications depend on data quality, appropriate algorithms, computing resources, and professional knowledge. The article considers the broader implications of combining intelligent methods with established geospatial workflows. It suggests that the future of digital geoinformation will increasingly depend on systems that can process large amounts of spatial information and transform it into useful knowledge. The discussion highlights both the potential and the practical challenges of this development and places Geo-IT and AI within the continuing evolution of digital technologies.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). This permits anyone to share, copy, redistribute, remix, transform, and build upon the material for any purpose, including commercial purposes, provided that appropriate credit is given to the original author(s), a link to the license is provided, and any changes made to the material are indicated.



