Generative AI and large language models: The key to creating intelligent, sustainable, and connected cities of the future

This review paper explores how Generative AI (GAI) and Large Language Models (LLMs) have the potential to reshape smart cities in the industry 5.0 era. By examining case studies and relevant literature, we analyze the influence of these technologies on industrial operations and urban management. The paper focuses on GAI as a key tool for optimizing industries and enabling predictive maintenance, while demonstrating how experts can leverage LLMs to enhance municipal services and communication with citizens. It also discusses the practical and ethical challenges of implementing these technologies. Additionally, the paper highlights emerging trends, illustrated through real-world examples ranging from factories to city-wide pilot projects, and identifies potential pitfalls. The widespread adoption of GAI faces obstacles such as infrastructure constraints and the lack of specialized knowledge needed for effective implementation. While LLMs open new opportunities for citizen services in smart cities, they also raise concerns about privacy, which this study seeks to address. Finally, the paper suggests future research areas, including the development of new ethical AI frameworks and long-term studies on the societal impacts of these technologies. This paper serves as a starting point for industrial leaders and urban developers to navigate the complexities of integrating GAI and LLMs, balancing technological innovation with ethical considerations.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-30
DOI
https://doi.org/10.5281/zenodo.17189841
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

Generative AI and large language models: The key to creating intelligent, sustainable, and connected cities of the future

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Generative AI and large language models: The key to creating intelligent, sustainable, and connected cities of the future

article en

Abstract

This review paper explores how Generative AI (GAI) and Large Language Models (LLMs) have the potential to reshape smart cities in the industry 5.0 era. By examining case studies and relevant literature, we analyze the influence of these technologies on industrial operations and urban management. The paper focuses on GAI as a key tool for optimizing industries and enabling predictive maintenance, while demonstrating how experts can leverage LLMs to enhance municipal services and communication with citizens. It also discusses the practical and ethical challenges of implementing these technologies. Additionally, the paper highlights emerging trends, illustrated through real-world examples ranging from factories to city-wide pilot projects, and identifies potential pitfalls. The widespread adoption of GAI faces obstacles such as infrastructure constraints and the lack of specialized knowledge needed for effective implementation. While LLMs open new opportunities for citizen services in smart cities, they also raise concerns about privacy, which this study seeks to address. Finally, the paper suggests future research areas, including the development of new ethical AI frameworks and long-term studies on the societal impacts of these technologies. This paper serves as a starting point for industrial leaders and urban developers to navigate the complexities of integrating GAI and LLMs, balancing technological innovation with ethical considerations.

Zenodo (CERN European Organization for Nuclear Research)
Lagos State University (NG), Akwa Ibom State University (NG), Federal University Oye Ekiti (NG)
Industry, innovation and infrastructure
Openalex Percentile: Top 28%
Geochemistry and Geologic Mapping
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Generative AI and large language models: The key to creating intelligent, sustainable, and connected cities of the future · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS