AI in spatial development - Where do we stand?; [KI in der Raumentwicklung – wo stehen wir?]

Abstract

This article considers the current position of artificial intelligence in spatial development and examines how AI can contribute to planning processes. Spatial development involves complex relationships between land use, infrastructure, population, environment and economic activity. Large amounts of spatial and statistical information are available, but turning these data into useful planning knowledge can require significant effort. Artificial intelligence can support this work through pattern recognition, classification, prediction and automated processing of large datasets. The article discusses where AI can provide practical benefits and where limitations remain. AI methods may help planners identify relationships in data, compare scenarios or process information that would be difficult to handle manually. However, spatial planning decisions often involve social, political and legal considerations that cannot be reduced to a purely technical calculation. The quality and representativeness of input data are also important because AI systems can reproduce weaknesses or biases contained in the underlying datasets. The article therefore places AI within the existing planning workflow and emphasizes the continued role of professional expertise. Transparent methods, understandable results and human review are important when AI-supported analyses are used to influence development decisions. The discussion shows that AI has significant potential in spatial development, but practical adoption requires suitable data, clear objectives and responsible implementation. Rather than replacing planners, AI can become a supporting technology that helps process information and explore options while leaving final decisions to qualified professionals and responsible institutions. These considerations suggest that the most useful AI applications are those that complement established planning methods and provide understandable evidence rather than producing unexplained recommendations.