Speakers
Description
For spatial planning, artificial intelligence (AI) offers considerable potential for the efficient categorisation and analysis of extensive data sets; however, it can also be used to solve problems logically, facilitate the systematic evaluation of historical data and enable intelligent search processes that can be used to derive more efficient solutions for spatial planning practice (Popelka et al., 2023; Son et al., 2023). The increasing use of AI will thus change municipal administrative and spatial planning processes and make them more effective, leading to an improvement in environmental conditions and promoting greater participation of residents in decision-making (Cugurullo et al., 2024; Wu et al., 2024). At the centre of AI capabilities is "machine learning", which recognises patterns, creates models and learns from large data sets using automated statistical methods. Based on big data, AI systems learn to perform more precise analyses and synthesise data using automated statistical methods. Overall, AI systems can facilitate the day-to-day tasks of planners and support interaction between stakeholders as well as decision-making within projects. But what impact will this have on spatial planning and the future of cities? Could AI eventually take over the role of planners and actively shape planning processes?
Various AI systems have already found their way into planning, for example in technologically orientated areas such as mobility and transport planning, energy and infrastructure planning or urban planning, which offer a wide range of possible applications for AI. So far, their use is often still prototypical and rarely firmly established. In particular, technologies such as digital platforms, spatial analysis tools, image-generating AI tools or chatbots are currently being used in planning processes to control urban services or infrastructure systems, monitor public spaces, derive scenarios for spatial development or create realistic design variants (e.g. Park et al., 2023; Zheng et al., 2023).
Based on a systematic literature review, supplemented by case studies and expert interviews, the presentation will classify AI technologies used in planning and analyse their application potential in different phases of the planning process. The results show that AI systems are specialised tools that have been developed to overcome specific challenges within planning processes. While these tools can improve the efficiency and outcomes of planning efforts, they are primarily used to suggest alternatives and solutions rather than dictate decisions. Therefore, AI should be seen as a planning aid rather than a replacement for human expertise. Ultimately, planners remain responsible for making informed decisions about urban development, weighing up the possibilities and limitations of AI applications in the planning process.
References
Cugurullo, F., Caprotti, F., Cook, M., Karvonen, A., McGuirk, P., & Marvin, S. (2024). Con-clusions. The present of urban AI and the future of cities. In F. Cugurullo, F. Caprotti, M. Cook, A. Karvonen, P. McGuirk, & S. Marvin (Eds.), Artificial intelligence and the city. Urban-istic perspectives on AI (pp. 361–389). Routledge.
Park, C., No, W., Choi, J., & Kim, Y. (2023). Development of an AI advisor for conceptual land use planning. Cities, 138, Article 104371. https://doi.org/10.1016/j.cities.2023.104371
Popelka, S., Narvaez Zertuche, L., & Beroche, H. (2023). Urban AI guide. Urban AI. https://doi.org/10.5281/zenodo.7708833
Son, T. H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J. M., & Mehmood, R. (2023). Algorithmic urban planning for smart and sustainable development: Systematic review of the literature. Sustainable Cities and Society, 94, Article 104562. https://doi.org/10.1016/j.scs.2023.104562
Wu, P., Zhang, Z., Peng, X., & Wang, R. (2024). Deep learning solutions for smart city chal-lenges in urban development. Scientific Reports, 14, Article 5176. https://doi.org/10.1038/s41598‐024‐55928‐3
Zheng, Y., Lin, Y., Zhao, L., Wu, T., Jin, D., & Li, Y. (2023). Spatial planning of urban commu-nities via deepreinforcement learning. Nature Computational Science, 3, 748–762. https://doi.org/10.1038/s43588‐023‐00503‐5
| Keywords | digitalisation, digital transformation, artificial intelligence, urban planning, planning phases |
|---|---|
| Best Congress Paper Award | Yes |
