Three-dimensional (3D) point analysis and visualization is one of the most effective methods of point cluster detection and segmentation in geospatial datasets. However, serious scattering and clotting characteristics interfere with the visual detection of 3D point clusters. To overcome this problem, this study proposes the use of 3D Voronoi diagrams to analyze and visualize 3D points instead of the original data item. The proposed algorithm computes the cluster of 3D points by applying a set of 3D Voronoi cells to describe and quantify 3D points. The decompositions of point cloud of 3D models are guided by the 3D Voronoi cell parameters. The parameter values are mapped from the Voronoi cells to 3D points to show the spatial pattern and relationships; thus, a 3D point cluster pattern can be highlighted and easily recognized. To capture different cluster patterns, continuous progressive clusters and segmentations are tested. The 3D spatial relationship is shown to facilitate cluster detection. Furthermore, the generated segmentations of real 3D data cases are exploited to demonstrate the feasibility of our approach in detecting different spatial clusters for continuous point cloud segmentation.

Natural resource management has entered a new stage in 2018 in the People’s Republic of China (China) marked by the establishment of the Ministry of Natural Resources of China (MNRPRC). More functions and responsibilities are integrated in the MNRPRC to build a uniform management system for full natural resource features in China with the aim of implementing uniform spatial planning and regulation, management, use and control, surveying, and registration for full natural resources. This paper first provides a detailed analysis regarding full natural resources with the perspectives of spatial forms and rights, restrictions, and responsibilities (RRRs); then, the modelling foundation of the “uniform” concept in natural resource registration is reconsidered. Lastly, we put forward a basic conceptual model for the uniform registration of full natural resources based on LADM (Land Administration Domain Model).

The urban low-altitude airspace is an integral part of urban space. As low-altitude utilization activities are being performed closer to the land surface, the management of the low-altitude space has become a focus of land administration. The management of the low-altitude airspace faces challenges such as cross-departmental coordination, fuzzy airspace boundaries, and complex spatial expressions. In 2020, the concept of “3D land administration” was introduced, marking the emergence of three-dimensional geospatial regulation in land management practices. Semantic models featuring the LADM (Land Administration Domain Model) as their core are updated iteratively to promote various applications related to 3D geographic regulation, but there is still a gap in the research on low-altitude utilization. Drawing upon Chinese regulations and policies, this paper employs the LADM framework to achieve semantic descriptions and expressions for managing areas in the low-altitude airspace: (1) relevant policies governing low-altitude spaces in China are analyzed, and the boundary between low-altitude and surface management is discussed; (2) the LADM structure is utilized to establish a comprehensive model for regulating low-altitude spaces; (3) and the capability of the LADM to support 3D low-altitude modeling is demonstrated through practical use cases in Shenzhen, China. Finally, the paper provides a comprehensive overview of the avenues for improvement and prospects.

(1) Background: The smart city management system, with GIS technology as its core, is based on realistic visualization of multiple types of 3D model data syntheses. However, the efficiency barriers to achieving smooth and continuous visualization from outdoor scenes to small indoor scenes remain a challenge. (2) Methods: This paper uses the visibility prediction method to obtain potential visual sets at three levels—outdoor, indoor and outdoor connection, and indoor—and constructs an R-tree spatial index structure for organizing potential visual sets. By integrating these potential visible sets with spatial indexes, scene visualization can be carried out effectively. (3) Results: A near-reality indoor and outdoor scene was used for experimentation, resulting in stable 10% fluctuation visual frame rates around 90 FPS. (4) Conclusions: Spatial indexing methods that combine potential visible sets can effectively solve the continuity and stability problem of indoor and outdoor scene visualization in smart city management systems.

3D (three-dimensional) property volume is an important data carrier for 3D land administration by using 3D cadastral technology, which can be used to express the legal space (property rights) scope matching with physical entities such as buildings and land. A 3D property volume is represented by a dense set of 3D coordinate points arranged in a predefined order and is displayed alongside the parcel map for reference and utilization by readers. To store a 3D property volume in the database, it is essential to record the connectivity relationships among the original 3D coordinate points, the associations between points and lines for representing boundary lines, and the relationships between lines for defining surfaces. Only by preserving the data structure that represents the relationships among points, lines, and surfaces can the 3D property volume in a parcel map be fully reconstructed. This approach inevitably results in the database storage volume significantly exceeding the original size of the point set, thereby causing storage redundancy. Consequently, this paper introduces a reversible 3D property volume compression coding method (called 3DPV-CC) to address this issue. By analyzing the distribution characteristics of the coordinate points of the 3D property volume, a specific rule for sorting the coordinate points is designed, enabling the database to have the ability of data storage and recovery by merely storing a reordered point set. The experimental results show that the 3DPV-CC method has excellent support capabilities for 3D property volumes of the vertical and slopped types, and can compress and restore the coordinate point set of the 3D property volume for drawing 3D parcel maps. The compression capacity of our method in the test is between 23.66% and 38.42%, higher than the general data compression methods (ZIP/7Z/RAR: 8.37–10.32%). By means of this method, land or real estate administrators from government departments can store 3D property volume data at a lower cost. This is conducive to enhancing the informatization level of land management.