Large Language Models for Sustainable Urban Development

by Nitin Liladhar Rane (Editor), Suraj Kumar Mallick (Editor), Jayesh Rane (Editor), Chaitanya Baliram Pande (Editor)

Overview

With rapid urbanization defining the 21st Century, cities face mounting challenges in achieving sustainability, equity, and functionality. This book explores how innovative technologies such as Artificial Intelligence (AI) and Large Language Models (LLMs) can transform urban development by offering intelligent, data-driven solutions. LLMs go beyond automation, acting as co-creators in addressing environmental sustainability, resource management, and equitable development. By analyzing regulations, best practices, and real-time data on phenomena such as air pollution and traffic, these models empower urban planners to design smarter, more sustainable cities while fostering collaboration across disciplines.

Divided into five sections, the book explores the diverse applications of LLMs, from optimizing renewable energy systems and enhancing urban planning to revolutionizing construction practices and improving resource efficiency. It highlights case studies on integrating AI with smart infrastructure, ecological balance, and disaster resilience. While underscoring their transformative potential, the book also examines ethical considerations such as bias, privacy, and environmental impact. More than a collection of research, this work is a call to action for urban planners, data scientists, policymakers, and researchers to harness AI responsibly in building greener, more equitable urban futures.

Product Details

ISBN-13:	9783031860386
Publisher:	Springer Nature Switzerland
Publication date:	07/30/2025
Series:	The Springer Series in Applied Machine Learning
Pages:	454
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Nitin Liladhar Rane serves as an Associate Editor for Frontiers in Education, and a reviewer for Taylor and Francis journals such as Asian Architecture and Building Engineering, Architectural Engineering and Design Management, Advances in Materials and Processing Technologies, Journal of Human-Computer Interaction, Cogent Engineering, Cogent Education, and Automatika. He has authored more than 40 research papers in various international journals, including Springer, Elsevier, Taylor and Francis with over 4500 citations to his name. He serves as a project evaluator for the MUNIS BIRD Programme, implemented by the World bank and the Ministry of Higher Education, Science and Innovations. His research focuses on material characterization, structural analysis, resilient environments, urban planning, remote sensing, GIS, and environmental monitoring and assessment.

Dr. Suraj Kumar Mallick is an Assistant Professor at the Department of Geography, Shaheed Bhagat Singh College, University of Delhi, New Delhi with a PhD degree in Urban Planning from Vidyasagar University. Before joining the faculty position, he was a Research Associate in the Department of Geography, Delhi School of Economics (DSE), University of Delhi, New Delhi. Later he joined as a Post-Doctoral Fellow of Centre for Technology Alternatives for Rural Areas (CTARA), Indian Institute of Technology, Bombay.

Prof. Jayesh Rane is Assistant Professor at Pillai HOC College of Engineering and Technology. His areas of interests include Image and Video processing, Artificial Intelligence and Internet of Things. He is author of Person Identification System using Handwritten signatures by ANN - A Neural Network Perspective (LAP Academic Publishing). He is a member of National Apex Professional body of Electronics and Telecommunication, Computer Science and IT Professionals (IETE) and Indian Society for Technical Education (ISTE) professional societies.

Dr. Chaitanya B. Pande is a Research Fellow at the International University. His research interests include remote sensing, GIS, Google Earth Engine, machine learning, watershed management, hydrogeology, hydrological modelling, drought monitoring, land use and land cover analysis, groundwater quality, urban planning, hydro-geochemistry, groundwater modelling, geology, hyperspectral remote sensing, remote sensing and GIS application in natural resources management, watershed management and environmental monitoring and assessment subjects.

Table of Contents

Section 1: Foundations of Large Language Models in Sustainable Urban Development.- Chapter 1 Impact of Large Language Models (LLMs) and Artificial Intelligence (AI) on renewable and sustainable energy.- Chapter 2 Harnessing artificial neural networks and large language models for enhanced urban energy planning: Improving annual performance of grid-connected high-power photovoltaic plants.- Chapter 3 Large language models for energy forecasting and prediction in renewable energy systems.- Section 2: Applications in Renewable Energy and Environmental Sustainability.- Chapter 4 Environmental monitoring and sustainability: LLMs for climate-responsive urban design.- Chapter 5 Assessing toxic chemical contamination in drinking water: Employing large language models to understand urban health impacts for sustainable development.- Chapter 6 Assessing reliability of large language model outputs on drinking water quality data from smart water distribution system.- Section 3: Urban Planning and Green Spaces.- Chapter 7 Transforming urban green spaces: The impact of large language models on smart and sustainable urban plantations.- Chapter 8 How large language models transform urban planning and shape tomorrow’s cities?.- Chapter 9 Chatting with your zoning code: Leveraging LLMs for real estate development.- Section 4: Smart and Sustainable Construction.- Chapter 10 Integration of Large Language Model (LLM) and Building information modeling (BIM) for enhanced construction project lifecycle management: A review.- Chapter 11 Large language models and artificial intelligence in the construction industry: Applications, opportunities, challenges, and ethical implications.- Chapter 12 Large language models for sustainable building design: Enhancing energy efficiency and material optimization.- Section 5: Data-Driven Decision Making in Urban Development.- Chapter 13 Smart and sustainable urban development: The pivotal role of large language models in data-driven decision making.- Chapter 14 Integrating biological frameworks into smart urban scaling through large language models.- Chapter 15 Investigating the relationship between land surface temperature and land use land cover change using spectral indices and LLM in Kosi river basin of Uttarakhand Himalaya.- Chapter 16 Accelerating road maintenance and repair processes: YOLO and Large Language Model for detection and classification of defects in flexible pavements.