How can artificial intelligence be used in spatial decision-making? The lecture showed that combining public data, map layers, and specialized tools helps municipalities and cities find sites for housing or kindergartens and uncover risks before construction even begins. An example is the temporary replacement for the cadastre that was created during its outage.
AI that relies on data, not gut feelings
In spatial decision-making, a parcel map alone is not enough; demographic indicators, data on economic activity, and information about history and planned development are needed. The platform gathers, processes, and continuously updates these public data in one place. This makes it possible to compare locations across time and space and back decisions with verifiable numbers.
It includes an AI assistant that answers natural questions without "hallucinating," because it draws on internal databases, not random web pages. You can ask, for example, how many transit services pass through a city district or what is going to be built nearby. The result is not only a textual answer but also a map with context.
From parcel selection to construction monitoring
For rental housing or other projects, there is a parcel finder by zoning plan, size, and other parameters that generates a shortlist of suitable locations. Then a variant generator quickly estimates how many apartments can be built on the site. From the available data, you can assemble a summary report with key demographic and economic indicators as well as potential risks.
Monitoring of future construction tracks official notice boards of approximately 700 municipalities and other registries to capture building permits or petitions and display them on a map. This produces an overview of where new interventions in the area are being prepared and what impact they may have. It is complemented by monitoring of the residential market from the websites of development projects in Slovakia and the Czech Republic, including financing terms, standards, and benefits.
