From Urban 3D Imagery to Low-Altitude Flight Risk Perception: A Construction Method for the Low-Altitude Flight Safety Zones of Surveying and Mapping UAVs and Its Application in Shanghai
Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations
Abstract
Abstract. With the in-depth penetration of Unmanned Aerial Vehicle (UAV) technology in fields such as geographic information surveying and mapping, the urban low-altitude economy has ushered in a critical opportunity for rapid development. However, surveying and mapping UAVs are confronted with the core technical bottleneck of "accurately determining the safety of flight routes", while issues such as airspace congestion and collision risks have become increasingly prominent. How to enable UAVs to perceive the complex environment in which they operate has thus emerged as a key challenge. Based on remote sensing images, 3D geographic data, and other relevant datasets, this study comprehensively applies multi-source data fusion technology to construct the theoretical framework and technical system of the "Urban Low-Altitude Surveying UAV Flight Safety Zones". It classifies the core causal factors affecting flight safety into three major categories: building height, electromagnetic interference, and controlled areas. Through a series of key technical processes, a standardized 3D spatial grid system is established, and a computable "risk perception" model is developed. Taking Shanghai Municipality as the empirical research area, empirical analysis is conducted using remote sensing images, ultimately generating a low-altitude flight safety field grid dataset covering the entire administrative region of Shanghai. Verification results indicate that this achievement can effectively identify the spatial distribution characteristics of potential safety hazards and height constraints, significantly reduce the collision probability between UAVs and obstacles, and provide standardized technical support for the flight safety of low-altitude surveying UAVs.
Abstract. UAS photogrammetry has become an efficient solution for acquiring high-resolution geospatial data for urban mapping, environmental monitoring, and 3D modelling. However, mission planning still involves a trade-off between data quality and operational efficiency, particularly regarding flight altitude, which directly affects ground sample distance (GSD), point cloud density, and positional accuracy. This study evaluates the influence of flight altitude through a controlled comparison of two urban photogrammetric surveys: a low-altitude flight at 61.2 m (GSD = 1.56 cm/pix, 420 images) and a higher-altitude flight at 121 m (GSD = 3.11 cm/pix, 116 images). Both surveys used RGB cameras with equivalent image resolution mounted on different platforms, which constitutes an experimental limitation, while overlap and processing parameters were kept constant. The results show that the lower-altitude flight produced denser data and better geometric performance, with lower reprojection error and lower check point RMSE. In contrast, the higher-altitude flight provided greater operational efficiency, covering a larger area with fewer images and lower computational demand. These findings indicate that both strategies are technically viable but suited to different objectives: lower altitudes favour geometric detail and positional accuracy, whereas higher altitudes improve productivity and area coverage. Therefore, flight altitude should be selected according to project requirements, balancing geometric quality and operational efficiency.
Emanuel Amorim, Diogo Inojosa, Jailson Rodrigues Júnior et al.· The International Archives o...· 0 citations
Remote sensing is widely recognized as a key technology across a wide range of technical and scientific domains, especially in agriculture. Although satellite data have long supported crop monitoring, their limitations in spatial resolution, revisit frequency and cloud coverage have often constrained their applications. High-resolution satellites, available from the beginning of the 2000s, have improved performance, particularly in the field of precision agriculture, but they remain expensive and inflexible. Unmanned Aerial Vehicles perform better in precision agriculture, offering flexibility and high levels of detail; however, their limited operational areas and short endurance flight times constrain their effectiveness. In this evolving landscape, High Altitude Pseudo Satellites (HAPSs), particularly high-altitude balloons, are emerging as a promising new technology that could fill the gaps between satellite and drone remote sensing. These platforms provide large area coverage with high-resolution imagery and long endurance flights at low operational expenses and ease of deployment. This study investigates the operational characteristics, strengths, and geometric limitations of data acquired by the CubeHAPS® platform, a high-altitude pseudo-satellite system, as a prerequisite for its application in precision agriculture. Focusing on experimental campaigns conducted in northern Italy in summer 2024 and 2025, the research characterizes platform stability, image block consistency, and photogrammetric quality through internal metrics. The results demonstrate measurable improvements between the two campaigns, attributed to the introduction of a stabilization system in 2025 and establishing the conditions under which the platform can support reliable photogrammetric reconstruction.
Lorenza Bovio, Victor Miherea, Jannis Fath et al.· Geomatics· 0 citations
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.· The International Archives o...· 0 citations
Unmanned Aerial Vehicle (UAV) photogrammetry is increasingly used for local-scale mapping because it enables rapid generation of high-resolution orthophotos and elevation products. However, the positional and vertical quality of these products depends strongly on the georeferencing strategy, particularly the use of Ground Control Points (GCPs). This study evaluates the effect of GCPs on the accuracy of UAV-derived orthophoto, Digital Surface Model (DSM), and Digital Terrain Model (DTM) at the premises of the Land Management Training Center (LMTC), Dhulikhel, Nepal. The same UAV image block of 334 images was processed under two workflows: one using only onboard image geolocation and another using five surveyed GCPs. Eight independent checkpoints were used for accuracy assessment. Orthophoto planimetric accuracy was evaluated using checkpoint coordinates, while vertical agreement of DSM and DTM was assessed using surveyed ground elevations. In addition, pairwise checkpoint-distance analysis and supplementary object-based comparison were used to examine relative geometric differences in the orthophotos. The with-GCP orthophoto achieved a horizontal RMSEH of 0.063 m, whereas the without-GCP orthophoto showed an RMSEH of 3.507 m. Similarly, the with-GCP DSM and DTM achieved RMSEZ values of 0.131 m and 0.143 m, respectively, compared with 15.385 m and 15.347 m in the without-GCP workflow. The without-GCP orthophoto also exhibited systematic westward and northward displacement and minor scale-related geometric differences. The results demonstrate that GCP-based processing remains essential for reliable campus-scale UAV photogrammetry when the outputs are intended for measurement, terrain representation, planning, and other applications requiring dependable absolute accuracy.
B. Bisht, Nabraj Subedi, Ram Kumar Sapkota et al.· Journal of Land Management a...· 0 citations
Urban tree mapping is necessary in environmental sustainability and climate change mitigation, and it depends heavily on the individual tree recognition and canopy segmentation to analyze city green cover. This systematic review discusses recent developments in the 2014–2026 mapping of these trees with the use of UAVs and high-resolution satellite imagery. Our preliminary selection of 4148 records reduced to 101 eligible publications following a systematic screening and synthesis of the records, assessed the efficiency of deep learning models such as Convolutional Neural Networks and Vision Transformers in processing various source images. We also compare object detection and semantic segmentation to see which one is more competent to deal with typical urban challenges, including overlapped canopies and building shadows. According to the reviewed studies, UAV-based models generally achieve higher spatial accuracy than satellite-based approaches for individual tree detection and crown delineation, with reported average Intersection over Union (IoU) values of approximately 70–75%, whereas satellite imagery provides superior spatial coverage for large-scale urban forest monitoring. Lastly, we present a research roadmap to address the existing weaknesses such as geographic bias, which propels the research direction towards multimodal data fusion and Foundation Models to sustain consistent, large-scale urban forest monitoring.
Syndar Satbayev, D. Yedilkhan, A. Shoman et al.· Journal of Imaging· 0 citations
Border security operations increasingly require intelligence tools that can deliver rapid, reliable insight across difficult terrain and under real operational constraints. This study evaluates an integrated UAV-GEOINT framework that combines LiDAR, high-resolution RGB imagery, GIS-based spatial analysis, and AI-driven object detection to support decision-making in complex border environments. A proof-of-concept field trial conducted in southeastern Thailand assessed how the multi-sensor system performed during realistic surveillance tasks involving concealed personnel and vehicles. Results show that the platform generated accurate 3D terrain models, detected targets with strong reliability, and reduced intelligence delivery time compared with current workflows. While dense canopy and large LiDAR data volumes introduced certain limitations, the trial demonstrated that fusing UAV sensing with automated analytics can meaningfully enhance situational awareness and operational responsiveness. These findings highlight the potential of integrated UAV-GEOINT capabilities to strengthen surveillance effectiveness in regions where traditional methods face persistent challenges.
Cattleya Delmaire, S. Malisuwan· 2026 6th International Confe...· 0 citations
