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TYPO-TOPO: AI-DRIVEN STRATEGIES FOR IDENTIFYING NOVEL, SUSTAINABLE URBAN TYPOLOGIES ACROSS DIVERSE TOPOGRAPHIES

Roman Hajtmanek, Viliam Zajíček, Alexander Kupko

First published: 2025-08-15https://doi.org/10.5593/sgem2025/6.1/s26.61View metrics

Abstract

This paper presents two AI-based case studies aimed at enhancing data-driven approaches to urban planning in relation to diverse topographical conditions towards sustainable development strategies and new urban typologies. The first case study employed a genetic algorithm to identify optimal spatial arrangements for agriculture, photovoltaic energy generation, and urban structure development. The analysis was conducted across three distinct Slovak topographies: riverlands, mountainous areas, and the existing urban fabric of a small town using high-resolution classified laser-scanned terrain data and parameters such as solar gain, slope, elevation, and orientation. The second case study explored the use of artificial neural networks trained on the spatial relationships between these topographical features and current urban patterns in mountainous regions. This model was then used to generate predictive scenarios for future development or propose new urban forms in currently non-urbanized areas. The research demonstrates how AI can support sustainable urban development through predictive modelling, optimization, and integration of environmental data into the early stages of spatial and architectural design.

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Publication details

Title
TYPO-TOPO: AI-DRIVEN STRATEGIES FOR IDENTIFYING NOVEL, SUSTAINABLE URBAN TYPOLOGIES ACROSS DIVERSE TOPOGRAPHIES
Authors
Roman Hajtmanek, Viliam Zajíček, Alexander Kupko
Proceedings
25th International Multidisciplinary Scientific GeoConference Proceedings SGEM 2025, Nano, Bio, Green, and Space Technologies for a Sustainable Future, Vol25, Issue 6.1
Publisher
STEF92 Technology
Year
2025
Pages
435-442
SWS Citekey
Hajtmanek202526435442
ISSN
1314-2704; 13142704
ISBN
9786197603873
Language
en
Publication type
Conference Paper
Proceedings contents
Open official contents
Keywords
artificial intelligenceurban planninggenetic algorithmartificial neural networktopographyspatial optimizationsustainable urban development
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