Digital Twin Buildings: 3D Modeling, GIS Integration, and Visual Descriptions Using Gaussian Splatting, ChatGPT/Deepseek, and Google Maps Platform

Kyle Gao, Dening Lu, Liangzhi Li, Nan Chen, Hongjie He +2 more
2/9/2025
cs.CV

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Abstract

Urban digital twins are virtual replicas of cities that use multi-source data and data analytics to optimize urban planning, infrastructure management, and decision-making. Towards this, we propose a framework focused on the single-building scale. By connecting to cloud mapping platforms such as Google Map Platforms APIs, by leveraging state-of-the-art multi-agent Large Language Models data analysis using ChatGPT(4o) and Deepseek-V3/R1, and by using our Gaussian Splatting-based mesh extraction pipeline, our Digital Twin Buildings framework can retrieve a building's 3D model, visual descriptions, and achieve cloud-based mapping integration with large language model-based data analytics using a building's address, postal code, or geographic coordinates.

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Cite this paper

@article{gao2025digital,
  title  = {Digital Twin Buildings: 3D Modeling, GIS Integration, and Visual Descriptions Using Gaussian Splatting, ChatGPT/Deepseek, and Google Maps Platform},
  author = {Kyle Gao and Dening Lu and Liangzhi Li and Nan Chen and Hongjie He and Linlin Xu and Jonathan Li},
  year   = {2025},
  eprint = {2502.05769},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.05769v3}
}

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