{"id":11119,"date":"2026-03-04T14:33:54","date_gmt":"2026-03-04T13:33:54","guid":{"rendered":"https:\/\/eurocc.nscc.sk\/?p=11119"},"modified":"2026-03-04T14:33:54","modified_gmt":"2026-03-04T13:33:54","slug":"mestske-budovy-sa-prebudzaju-slovenska-ai-dava-druhu-sancu-nevyuzitym-priestorom","status":"publish","type":"post","link":"https:\/\/eurocc.nscc.sk\/en\/mestske-budovy-sa-prebudzaju-slovenska-ai-dava-druhu-sancu-nevyuzitym-priestorom\/","title":{"rendered":"Urban buildings awaken: Slovak AI gives a second chance to underused spaces<\/strong>"},"content":{"rendered":"
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Urban buildings awaken: Slovak AI gives a second chance to underused spaces<\/strong><\/p>\n\n\n\n

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Cities are living organisms that constantly evolve. Yet many of us pass daily by silent witnesses of the past in our neighborhoods\u2014empty schools, unused administrative buildings, or deteriorating public facilities. We often ask ourselves:\u00a0\u201cWhy is this closed?\u201d \u201cCouldn\u2019t this space serve as a day-care center, a kindergarten, or a cultural hub instead?\u201d<\/em><\/p>\n\n\n\n

Finding the right function for such a building, however, is not just a matter of having a good idea. It is a complex urban planning puzzle. This is precisely the challenge that a Slovak team from the organization Creative Industry Ko\u0161ice (CIKE) has set out to address. As part of the SAM-SUD (Smart Asset Management \u2013 Sustainable Urban Development) project, funded by the European Union, they are developing a tool called NextUseAI.\nTheir goal is to create an intelligent system that can help cities determine which functions best fit a given location, taking into account the overall urban structure, the priorities defined in municipal strategies, and\u2014most importantly\u2014the real needs of residents in that specific area.<\/p>\n\n\n\n

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Challenge: Millions of Microlocations in the Digital World<\/strong><\/p>\n\n\n\n

Planning a city according to people\u2019s needs means understanding space. The concept of the \u201c15-minute city\u201d suggests that everything essential should be accessible within walking distance. However, for artificial intelligence (AI) to provide meaningful guidance to cities, it must first process vast amounts of data about every street, sidewalk, and existing service.<\/p>\n\n\n\n

The Slovak team worked with data on the scale of hundreds of gigabytes, including map data from OpenStreetMap, digital elevation maps, and databases containing thousands of public amenities. The challenge was to transform these datasets into complex mathematical matrices of walking distances. <\/p>\n\n\n\n

In the initial phase of the project, we needed to identify weaknesses in our process through rapid iterations and quickly reach results, understand them, and then adjust the input parameters again. Even at this stage, we had to process substantial volumes of data in which the neural network could detect significant patterns. On a regular computer, this would have taken weeks. Without extreme computing power, it would not have been possible to achieve the first meaningful results in such a short time,\u201d says R\u00f3bert Poll\u00e1k, head of the NextUseAI research team. <\/p>\n\n\n\n

Solution: The Power of the MeluXina Supercomputer<\/strong><\/p>\n\n\n\n

The breakthrough came thanks to access to the European supercomputer MeluXina in Luxembourg, specifically to the part dedicated to artificial intelligence (the AI Factory). Here, Slovak experts were given access to powerful graphics accelerators capable of processing thousands of operations simultaneously.<\/p>\n\n\n\n

In this environment, the team built and tested advanced neural networks. These networks were trained to recognize relationships between buildings, services, and their surroundings across different cities.\nSupercomputing enabled us to experiment with different configurations and quickly fix errors, which would not have been possible under normal conditions. Thanks to this, we were able to rapidly generate a method for producing spatial recommendations for the two largest Slovak cities\u2014Bratislava and Ko\u0161ice. The models can learn from the spatial structure of one or multiple cities and provide recommendations for another city,\u201d adds Timotej Kendere\u0161, a data analyst at CIKE responsible for working with the MeluXina supercomputer.<\/p>\n\n\n\n

Results: Data in the Service of People<\/strong><\/p>\n\n\n\n

The result is not just a dry table of numbers. The AI model aims to propose concrete functions for underused urban spaces to city planners and strategists in order to improve public amenities and walkable accessibility within neighborhoods.\nNextUseAI will then evaluate spatial recommendations in the context of residents\u2019 needs and the city\u2019s strategic priorities, accompanied by a clear explanatory rationale. <\/p>\n\n\n\n

Although the results are currently still in the experimental phase and serve to calibrate the entire system, they have already demonstrated an important insight: artificial intelligence can detect patterns and connections that may escape human observation.\nFor example, the system can identify \u201cblind spots\u201d in a city\u2014areas where a particular service is missing\u2014and suggest placing it in a nearby underused building.<\/p>\n\n\n\n

Impact and Future Potential<\/strong><\/p>\n\n\n\n

NextUseAI does not end in the laboratory. Its ambition is to become a practical tool that helps city leaders make decisions based on data rather than intuition.<\/p>\n\n\n\n

For residents, this could mean in the future:<\/p>\n\n\n\n