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Open access Aug 2026

A Low-Altitude Data Space Framework Based on China’s National 3D Mapping Program

Abstract. The low-altitude economy has emerged as a strategic emerging industry in China, involving economic activities within airspace below 1,000 meters, such as manned/unmanned cargo/passenger transport. This three-dimensional economic form faces core challenges in ensuring flight safety, optimizing operational efficiency, and achieving large-scale commercialization, primarily due to the complexity of low-altitude environments characterized by dense urban structures, dynamic meteorological factors, and mixed aircraft operations. This demands innovative digital management solutions. A low-altitude three-dimensional data space, defined as a digitally integrated environment for organizing, managing, and applying multi-source heterogeneous data within this airspace, serves as a critical foundation for supporting safe, efficient, and intelligent development. This study proposes a comprehensive low-altitude three-dimensional data space framework grounded in China's National 3D Realistic Geospatial Landscape Model (3dRGLM). The framework establishes a spatiotemporally integrated digital environment supporting critical applications in low-altitude airspace planning, intelligent 3D navigation, and safety-guaranteed airspace management. It is structured around entity-based digital modeling of low-altitude elements, multi-source spatiotemporal data fusion via grid-discretized management, and trusted data circulation mechanisms. By leveraging 3dRGLM's capabilities in realistic 3D representation and dynamic geographic entity association, the proposed data space enables end-to-end digital twin modeling of low-altitude operational scenarios. Pilot programs in Wuhu and Deqing demonstrate the framework's effectiveness in enhancing decision-making precision for air route network design, 3D dynamic navigation, and risk-aware flight control, confirming its value as a replicable model for scalable low-altitude economy development.

Yin Gao, Jun Chen, Dehu Yang et al. · 0 citations
Review Open access Aug 2026

Dual-Module Bench-Line Extraction and Surface-Object Segmentation from UAV LiDAR Point Clouds in Open-Pit Mines Using Neighborhood Geometric Analysis and an Enhanced PointNet++ Network

Open-pit mines contain rapidly changing terrain, discontinuous bench structures, and mixed artificial–natural objects, which complicate automated three-dimensional mapping. This study presents a dual-module workflow for UAV LiDAR point clouds. Module A characterizes local geometry using normal and curvature descriptors, constructs local plane support through RANSAC fitting, and detects candidate bench-line points using an angular-gap criterion, followed by regional grouping and Kalman-filter refinement. Qualitative overlay with the orthophoto showed coherent correspondence with principal platform–slope transitions. Module B segments buildings, roads, and vegetation using a PointNet++ network enhanced by local Transformer self-attention and inverted residual feature transformation. Under a fixed spatial hold-out setting, the network achieved an overall accuracy of 97.6% and a mean intersection over union of 96.4%. It obtained the highest overall accuracy, mean intersection over union, and class-wise intersection over union among the selected baselines, whereas Point Transformer achieved a slightly higher mean class accuracy. The two independently operated modules provide complementary structural and semantic information for open-pit mine mapping. Broader applicability requires reference-based bench-line assessment and evaluation across additional mines and survey periods.

Shanfeng Ge, Nijia Qian, Jing-Xiang Gao et al. · 0 citations