Using digital twin models of tunnels has become critical to their efficient maintenance and management. A high-precision 3D tunnel model is the prerequisite for a successful digital twin model of tunnel applications. However, constructing high-precision 3D tunnel models with high-quality textures and structural integrity based on mobile laser scanning data remains a challenge, particularly for tunnels of different shapes. This study addresses this problem by developing a novel method for the 3D reconstruction of multi-shaped tunnels based on mobile laser scanning data. This method does not require any predefined mathematical models or projection parameters to convert point clouds into 2D intensity images that conform to the geometric features of tunnel linings. This method also improves the accuracy of 3D tunnel mesh models by applying an adaptive threshold approach that reduces the number of pseudo-surfaces generated during the Poisson surface reconstruction of tunnels. This method was experimentally verified by conducting 3D reconstruction tasks involving tunnel point clouds of four different shapes. The superiority of this method was further confirmed through qualitative and quantitative comparisons with related approaches. By automatically and efficiently constructing a high-precision 3D tunnel model, the proposed method offers an important model foundation for digital twin engineering and a valuable reference for future tunnel model construction projects.

Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood’s spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a “measuring table” to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information.

In complex urban low-altitude (ULA) airspace, unmanned aerial vehicles (UAVs) face several safety challenges, such as building obstacles, airspace restrictions, and environmental uncertainties. In this study, these issues are addressed by adopting a novel quantitative method for evaluating UAV flight safety margins and integrating this method into a ULA airspace grid model. This method comprehensively considers critical factors such as airspace obstacles, environmental conditions, and UAV performance to compute a quantitative safety margin. Once safety buffer grids around restricted and potential conflict grids are introduced, dynamic constraints can be imposed on the trajectory planning process. The proposed model not only achieves a balance between path cost and safety redundancy but also significantly enhances UAV flight safety and the efficiency of airspace resource utilization in complex urban environments. The experimental results validate the effectiveness of this approach for planning multi-UAV trajectories, demonstrating its feasibility and potential for broader application. This research not only extends the safety implications of low-altitude airspace grid modeling but also provides a new technical pathway and theoretical foundation for future ULA airspace safety management, multi-UAV collaborative scheduling, and refined airspace governance.

The development of the digital economy has highlighted the important value of geospatial data across numerous domains, with data trading being a pivotal link in activating this value. The current user base engaging in data trading is diverse, while trading platforms encounter problems such as disorganized data management and oversimplified retrieval methods. These concerns lead to inefficient retrieval for users with minimal domain knowledge. To address these complexities, this study proposes an intelligent retrieval method for geospatial data oriented toward data trading. This method establishes a geospatial data knowledge graph based on a standardized ontology model and innovatively utilizes large language models to assess user requirements in data trading. It effectively addresses the problems of standardized management for multi-source heterogeneous geospatial data and the poor adaptability of traditional retrieval methods to the ambiguous requirements of users lacking professional domain knowledge. Thus, it improves the efficiency and universality of geospatial data trading while guaranteeing the semantic interpretability of retrieval results. Experimental results confirm that the proposed method considerably outperforms traditional keyword-based retrieval methods. It exhibits particularly notable performance enhancements in scenarios with ambiguous requirements. This research not only effectively extends management approaches for geospatial data but also strengthens the inclusivity of data trading. Thus, it provides technical support for maximizing the value of geospatial data.