The Development and Construction of City Information Modeling (CIM): A Survey from Data Perspective

With rapid urbanization exacerbating the challenges in resource allocation, environmental sustainability, and infrastructure management, City Information Modeling (CIM) has emerged as an indispensable digital solution for smart city development. CIM represents an advanced urban management paradigm that integrates Geographic Information Systems (GISs), Building Information Modeling (BIM), and the Internet of Things (IoT) to establish a multidimensional digital framework for comprehensive urban data management and intelligent decision making. While the existing research has primarily focused on technical architectures, governance models, and application scenarios, a systematic exploration of CIM’s data-driven characteristics remains limited. This paper reviews the evolution of CIM from a data-centric view introducing a research framework that systematically examines the data lifecycle, including acquisition, processing, analysis, and decision support. Furthermore, it explores the application of CIM in key areas such as smart transportation and digital twin cities, emphasizing its deep integration with big data, artificial intelligence (AI), and cloud computing to enhance urban governance and intelligent services. Despite its advancements, CIM faces critical challenges, including data security, privacy protection, and cross-sectoral data sharing. This survey highlights these limitations and points out the future research directions, including adaptive data infrastructure, ethical frameworks for urban data governance, intelligent decision-making systems leveraging multi-source heterogeneous data, and the integration of CIM with emerging technologies such as AI and blockchain. These innovations will enhance CIM’s capacity to support intelligent, resilient, and sustainable urban development. By establishing a theoretical foundation for CIM as a data-intensive framework, this survey provides valuable insights and forward-looking guidance for its continued research and practical implementation.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2025-04-24
DOI
https://doi.org/10.3390/app15094696
Citations
8
Primary Topic
3D Modeling in Geospatial Applications
Type
article
Field-Weighted Citation Impact
8.05

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Development and Construction of City Information Modeling (CIM): A Survey from Data Perspective

Junyu Dong, Wei Yu, Dongsheng Wang, Xiaowei Zhou
8 citations
Applied Sciences
3D Modeling in Geospatial Applications
8.05
article

The Development and Construction of City Information Modeling (CIM): A Survey from Data Perspective

Junyu Dong, Wei Yu, Dongsheng Wang, Xiaowei Zhou
article en
8 citations

Abstract

With rapid urbanization exacerbating the challenges in resource allocation, environmental sustainability, and infrastructure management, City Information Modeling (CIM) has emerged as an indispensable digital solution for smart city development. CIM represents an advanced urban management paradigm that integrates Geographic Information Systems (GISs), Building Information Modeling (BIM), and the Internet of Things (IoT) to establish a multidimensional digital framework for comprehensive urban data management and intelligent decision making. While the existing research has primarily focused on technical architectures, governance models, and application scenarios, a systematic exploration of CIM’s data-driven characteristics remains limited. This paper reviews the evolution of CIM from a data-centric view introducing a research framework that systematically examines the data lifecycle, including acquisition, processing, analysis, and decision support. Furthermore, it explores the application of CIM in key areas such as smart transportation and digital twin cities, emphasizing its deep integration with big data, artificial intelligence (AI), and cloud computing to enhance urban governance and intelligent services. Despite its advancements, CIM faces critical challenges, including data security, privacy protection, and cross-sectoral data sharing. This survey highlights these limitations and points out the future research directions, including adaptive data infrastructure, ethical frameworks for urban data governance, intelligent decision-making systems leveraging multi-source heterogeneous data, and the integration of CIM with emerging technologies such as AI and blockchain. These innovations will enhance CIM’s capacity to support intelligent, resilient, and sustainable urban development. By establishing a theoretical foundation for CIM as a data-intensive framework, this survey provides valuable insights and forward-looking guidance for its continued research and practical implementation.

Applied SciencesVol. 15(9)
Ocean University of China (CN), Tsinghua University (CN)
Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 2%
3D Modeling in Geospatial Applications
8.05
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.