Tekoälyn soveltaminen kaavoituksessa ja laajat kielimallit paikkatietoanalyysien käyttöliittymänä. Kirjallisuuskatsaus ja tapaustutkimus
Year: 2025
Type: dissertation
Abstract: In recent years, interest in AI has grown significantly after the breakthrough of large language models. While AI’s potential for enabling potentially large gains in efficiency is acknowledged, it hasn’t yet been widely utilized in the field of zoning and land use planning, particularly in practical planning tasks. This Masters’ Thesis on Spatial Planning and Transportation Engineering aims to shed light on a research gap with a systematic literature review regarding practical applications of artificial intelligence in the context of zoning and land use planning, especially related to evaluating and monitoring the development of urban structure. In addition to the literature review, a pilot study was conducted on the utilization of large language models (LLM) as a user interface for generating spatial analyses related to urban planning. The literature review identified several potential AI applications suitable for classifying urban structure and identifying and monitoring its different characteristics. Based on the literature review, much of the potential of AI lies in enabling the use of new types of data formats, which can help to provide a more diverse and holistic understanding of urban structure and monitoring the changes within it. With this more diverse data, AI can help urban planners and developers create more holistic and inclusive plans. The research part of the thesis examined how well LLMs can produce spatial data analyses through PostGIS queries. The study found that this approach has potential, especially in simple and moderately difficult analyses, but there were challenges in producing difficult and complex analyses. The research shows that AI can be used to streamline and, in the future, even completely automate some routine tasks in the everyday work of urban planners, although more research is needed, especially in improving reliability and transparency.
Source: Aaltodoc (Aalto University)
Author: Salonen, Aapo
Language: English
Subfield: Urban Studies
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7076967428