## 6 Conclusions and Future Work  
This survey systematically reviews the evolution of CIM, with a particular emphasis on its data-driven characteristics. CIM integrates GISs, BIM, and the IoT to achieve comprehensive urban data collection, management, analysis, and decision support [83]. However, the current research predominantly focuses on technical architecture, governance models, and application scenarios, leaving significant gaps in areas such as data governance, data quality assurance, and multi-source data fusion [67,84]. To address these limitations, this article systematically examines the data lifecycle of CIM, encompassing data collection, processing, analysis, and decision support, and proposes a data-driven CIM research framework aimed at enhancing its intelligence in smart city development. The survey also explores the application of CIM in domains such as smart transportation, digital twin cities, environmental monitoring, and public safety, analyzing its integration trends with cutting-edge technologies like big data, AI, and cloud computing. Furthermore, this article highlights the critical challenges faced by CIM, including data security, privacy protection, and cross-departmental data sharing, and discusses its future development directions. These include the adaptation of urban data infrastructure, the development of intelligent decision-making systems, the creation of automated analysis models for multi-source heterogeneous data, and the deep integration of CIM with AI, blockchain, and other emerging technologies, offering forward-looking insights for future research and practice [85]. Despite its broad application potential across various urban management fields, CIM still faces several challenges. First, the diversity of urban data sources and the inconsistency in data formats necessitate robust solutions. Issues related to data quality, standardization, and consistency are critical and require further optimization of the data governance mechanisms. Second, achieving a balance between privacy protection and data sharing remains a significant challenge. Both technological and policy measures must be further explored to enable secure and efficient information sharing [86,87]. Additionally, the computational efficiency of CIM in processing large-scale datasets and performing real-time analysis needs improvement to meet the demands of city-level data processing, particularly in high-timeliness scenarios such as intelligent transportation management and emergency response. Future research can focus on the following areas: First, enhancing CIM's real-time processing capabilities for large-scale datasets by optimizing the computational architectures to support rapid analysis and intelligent decision making in complex, dynamic environments [88]. Second, improving the data standardization and integration processes to enhance the system compatibility and data quality, thereby facilitating the broader adoption of CIM across different cities and industries. The deep integration of CIM with AI will be a core research direction, leveraging AI technologies to strengthen the urban data governance capabilities and further explore the balance between data sharing, privacy protection, and social responsibility. Moreover, the effective integration of citizen-generated data (CGD) presents another promising avenue for research. Investigating how to utilize public participation data to improve the accuracy of urban management and the scientific basis of public decision making will be a critical issue in smart city development. Data-driven CIM has profound social and economic implications for smart city construction. By enhancing the intelligence and precision of urban management, CIM is expected to improve operational efficiency, optimize resource allocation, and elevate residents' quality of life [59].