Advanced User Interaction with Urban Digital Twins using Large Language Models
Large language models (LLMs), such as OpenAI's Generative Pre-trained Transformer (GPT), commonly known as ChatGPT has witnessed a very rapid evolution which has opened the door for new possibilities across various industries and academic fields. These advanced technologies are transforming how...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
|
| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-G-2025/469/2025/isprs-annals-X-G-2025-469-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849321067033657344 |
|---|---|
| author | K. Kanna T. H. Kolbe |
| author_facet | K. Kanna T. H. Kolbe |
| author_sort | K. Kanna |
| collection | DOAJ |
| description | Large language models (LLMs), such as OpenAI's Generative Pre-trained Transformer (GPT), commonly known as ChatGPT has witnessed a very rapid evolution which has opened the door for new possibilities across various industries and academic fields. These advanced technologies are transforming how we view and interact with data, how we communicate and solve complex problems. In this paper, we present a framework that employs LLMs to interact with an Urban Digital Twin (UDT) of a district. The framework utilizes the semantic richness of CityGML for representing 3D city models and the SensorThings API for managing dynamic sensor data, allowing users to query and visualize geospatial and dynamic information intuitively. Through experiments with different types of queries from stakeholders, varying from city planners, to utility providers, and citizens, we found that LLMs can effectively translate natural language queries into complex geospatial and temporal operations, narrowing the gap between non-expert users and complex urban datasets into a fine margin. The results shed light on the potential of LLMs to support decision-making in smart city applications. |
| format | Article |
| id | doaj-art-7af3a6289e8948678486e2bf5483dbe9 |
| institution | Kabale University |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-7af3a6289e8948678486e2bf5483dbe92025-08-20T03:49:50ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502025-07-01X-G-202546947610.5194/isprs-annals-X-G-2025-469-2025Advanced User Interaction with Urban Digital Twins using Large Language ModelsK. Kanna0T. H. Kolbe1Chair of Geoinformatics, Technical University of Munich, 80333 Munich, GermanyChair of Geoinformatics, Technical University of Munich, 80333 Munich, GermanyLarge language models (LLMs), such as OpenAI's Generative Pre-trained Transformer (GPT), commonly known as ChatGPT has witnessed a very rapid evolution which has opened the door for new possibilities across various industries and academic fields. These advanced technologies are transforming how we view and interact with data, how we communicate and solve complex problems. In this paper, we present a framework that employs LLMs to interact with an Urban Digital Twin (UDT) of a district. The framework utilizes the semantic richness of CityGML for representing 3D city models and the SensorThings API for managing dynamic sensor data, allowing users to query and visualize geospatial and dynamic information intuitively. Through experiments with different types of queries from stakeholders, varying from city planners, to utility providers, and citizens, we found that LLMs can effectively translate natural language queries into complex geospatial and temporal operations, narrowing the gap between non-expert users and complex urban datasets into a fine margin. The results shed light on the potential of LLMs to support decision-making in smart city applications.https://isprs-annals.copernicus.org/articles/X-G-2025/469/2025/isprs-annals-X-G-2025-469-2025.pdf |
| spellingShingle | K. Kanna T. H. Kolbe Advanced User Interaction with Urban Digital Twins using Large Language Models ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Advanced User Interaction with Urban Digital Twins using Large Language Models |
| title_full | Advanced User Interaction with Urban Digital Twins using Large Language Models |
| title_fullStr | Advanced User Interaction with Urban Digital Twins using Large Language Models |
| title_full_unstemmed | Advanced User Interaction with Urban Digital Twins using Large Language Models |
| title_short | Advanced User Interaction with Urban Digital Twins using Large Language Models |
| title_sort | advanced user interaction with urban digital twins using large language models |
| url | https://isprs-annals.copernicus.org/articles/X-G-2025/469/2025/isprs-annals-X-G-2025-469-2025.pdf |
| work_keys_str_mv | AT kkanna advanceduserinteractionwithurbandigitaltwinsusinglargelanguagemodels AT thkolbe advanceduserinteractionwithurbandigitaltwinsusinglargelanguagemodels |