## 1 Introduction
Conversely, when semantic features undergo alteration, the spatial positioning may remain unchanged [14]. The holistic lifecycle management of underground pipelines primarily manifests the temporal characteristics and involves comprehensive management and planning of the pipeline system across its design, construction, operation, maintenance, renewal, and decommissioning stages. This is to ensure the safety and efficient operation of the pipeline system while maximizing its lifespan. Across various lifecycle stages, the managing entities, processes, data sources, types, and required information differ. With the passage of time, the temporal attributes exhibit more pronounced characteristic changes [15]. Therefore, when modeling underground pipelines, it is imperative to consider their temporal properties from multiple perspectives and construct datasets containing multi-temporal features. First, there is a dataset used in the planning and approval stage, which provides support for formulating planning requirements, evaluating site feasibility, and refining design proposals through comparisons and simulations with modern spatial network datasets. Next, there is a dataset applied in the stages of engineering construction and facility maintenance, typically obtained from pipeline surveys. This dataset includes static information about the pipeline network and its associated features. It plays a crucial role in providing important data support for pipeline construction, operation, and maintenance. It assists managers in designing construction plans, monitoring progress, and guiding network operations. Furthermore, there is a dataset that records the status and events of the underground pipeline network at different time points. Through analyzing historical data, it identifies performance degradation trends in certain regions or materials within the network, thereby prioritizing maintenance and updates in these areas or pipelines. Lastly, there is a dataset consisting of dynamic and changing data collected from the underground pipeline network system. These data change as inspections, maintenance, repair management, and risk assessments are conducted. The temporal attribute information of these monitoring data is crucial for the subsequent maintenance of the pipeline network model. The semantic characteristics refer to the attributes of underground pipelines. These attributes include information such as pipeline material, diameter, depth of burial, purpose, departmental ownership, maintenance status, and pipeline connectivity. These semantic features can assist managers in better understanding the structure and functionality of pipelines, thereby enabling effective pipeline maintenance, planning, and decision-making. By analyzing the semantic characteristics of underground pipelines, comprehensive monitoring and management can be achieved, thereby enhancing the safety, reliability, and efficiency of the networks. The phenomenon of cognitive differences in describing underground pipeline network information across different fields is referred to as semantic heterogeneity [16]. Semantic heterogeneity may lead to data inconsistency, integration challenges, and analytical difficulties, thereby limiting the comprehensive understanding and effective management of the network system. To address the challenge of semantic heterogeneity in underground pipelines, it is necessary to adopt unified data standards and data processing techniques to facilitate interoperability and information sharing among different data sources, thereby improving management efficiency. Based on the above research, this paper takes the underground pipeline network as the research object and designs a spatial data fusion model for underground pipeline networks that can consider geometric, temporal, and semantic aspects to meet the demand for refined modeling of these underground pipeline networks. This fusion modeling approach adopts a holistic, integrated mindset.