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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
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| 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 |
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| Summary: | 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. |
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| ISSN: | 2194-9042 2194-9050 |