To meet the extreme measurement accuracy requirements of the Polymorphic Coupled Rail Transit Dynamic Model Test Platform under complex design conditions-namely ultra-high speeds of up to 1,500km/h and ambient pressures ranging from 0.005 to 1.0 standard atmospheres-this study addresses the lack of a systematic accuracy control theory for installation measurements of ultra-high-speed maglev vacuum-tube supports. As the fundamental positioning elements of the vacuum maglev tube system, support installations directly govern track smoothness and tube airtightness. However, a comprehensive accuracy control framework for this domain has yet to be established, substantially constraining reliable engineering implementation. Here, a "reverse" accuracy allocation strategy is proposed, taking support design parameters as the starting point and integrating the law of error propagation, Least Squares Collocation theory, and computer-based virtual observation simulations. This approach establishes a complete accuracy transfer chain, linking support design accuracy to measurement-control point accuracy, station-setting accuracy, and ultimately to the precision of the offline control network. On this basis, a three-dimensional free-station adjustment model for total station that incorporates the prior accuracy of known points is developed, effectively mitigating the influence of control-point errors on station-setting precision. Through dense grid-based simulation experiments, the spatial distribution characteristics of station-setting accuracy are systematically revealed, providing a theoretical foundation for optimal station placement in field operations. Case studies validate the feasibility and applicability of the proposed methodology, demonstrating that it delivers a rigorously derived accuracy control framework for measurement scheme design under varying tube parameters, instrument precisions, and control network configurations. The accuracy derivation system established in this work offers a complete technical pathway-from theoretical formulation to engineering application-for support installation measurements in China's ultra-high-speed vacuum maglev transportation systems. It also provides an important reference for promoting the standardized and theory-driven application of precision measurement technologies in major engineering projects.

ABSTRACT Unmanned aerial vehicles (UAVs) are increasingly used for post-conflict and disaster mapping because they enable rapid acquisition of high-resolution imagery and dense 3D point clouds in hazardous or inaccessible areas. This study evaluates the accuracy of UAV-derived point clouds for Kafranbel, Syria, a 143-hectare war-damaged urban area where rubble, vegetation, and damaged structures complicate ground control placement and photogrammetric reconstruction. Four UAV missions were conducted using a DJI Mavic 2 Pro: three nadir flights at 45 m, 50 m, and 65 m altitude with approximately 80% front and side overlap, and one oblique flight at 38 m with a 70° camera angle. The missions captured 400–1000 images each. Fifty-four independent checkpoints were surveyed using a Topcon 105N total station with <2 cm accuracy and used as external reference data for validation. Images were processed in Agisoft Metashape using a structure-from-motion workflow to generate dense point clouds, digital elevation models, and orthophotos. Accuracy was evaluated by computing RMSE in X, Y, Z, and RMSE3D. The 45 m nadir flight achieved the highest accuracy, with an RMSE3D of about 0.13 m, compared with about 0.35 m at 50 m and 0.45 m at 65 m. Planimetric errors in the nadir models were generally 5–10 cm. The 38 m oblique mission improved modelling of vertical structures such as façades and walls, although horizontal precision on flat surfaces decreased to about 14 cm. Overall, a flight altitude near 50 m provided the best compromise between accuracy, coverage, and efficiency. The results recommend combining nadir and oblique imagery with a well-distributed checkpoint network for reliable 3D documentation of war-damaged urban areas.

ABSTRACT Combining total station and camera, the system measures large dynamic objects. Current calibration mainly covers coaxial and external cameras, lacking research on the vital built-in overview camera of image-assisted total stations. This paper builds its image measurement model and develops overall and stepwise calibration methods. The overall method is unstable and range-limited, whereas stepwise calibration achieves effective parameter decoupling. On this basis, fourth-order bivariate Fourier APs are used for distortion compensation. After correction, MAE and Std reach 0.07 and 0.09 pixels in both x and y directions, offering reliable references for practical calibration.

Favelas (slums) are densely populated informal settlements that house over 1.1 billion people globally and represent challenging environments for spatial data acquisition and urban mapping. Their dense morphology, irregular street networks, and overlapping building structures make conventional mapping approaches difficult. These areas are often characterized by limited access to essential services such as water supply, sanitation, and waste collection, as well as insecure land tenure. Despite their scale, favelas are frequently absent from official maps, limiting their inclusion in urban planning processes. This Data Descriptor presents a terrestrial LiDAR dataset collected with five distinct devices along a standardized 360-meter survey route in Jardim Colombo, a favela in São Paulo, Brazil, covering approximately 5,685 m². The area was also surveyed using aerial photogrammetry and a total station to establish a spatial reference supporting comparison among the terrestrial LiDAR devices. A multicriteria framework evaluates device performance considering point density, completeness, spatial uniformity, and logistical constraints. These data support research in urban environments where accurate spatial representation remains a critical challenge.

Deformation monitoring is critical for structural health assessment of operational shield tunnels in urban rail transit. LiDAR point clouds in operating tunnels usually contain auxiliary facilities, occlusions, noise, and uneven point density. Conventional section-by-section ellipse fitting often leads to unstable parameter jumps between adjacent sections. This paper presents a high-precision method to extract lateral deformation from tunnel LiDAR point clouds. First, a point-wise attention Transformer network (PWAT) is proposed based on PointNet++ for lining segmentation, using k-NN adaptive sampling, geometric position encoding, and geometry-constrained multi-head self-attention. Second, a continuity-constrained RANSAC (CC-RANSAC) algorithm is developed to improve ellipse parameter stability by adding continuity penalties between neighboring sections. Experiments were carried out on a Shanghai metro shield tunnel. Results show that PWAT achieves 99.53% overall accuracy and 99.06% mIoU in six-class segmentation. CC-RANSAC reduces the mean residual to 2.0 mm and the center jump rate to 4.2%. Compared with total station data, the mean absolute error and root mean square error are 1.35 mm and 1.68 mm. The proposed method can automatically and accurately extract lateral deformation for operational shield tunnels.

Abstract Digital Elevation Models (DEM) are useful for solving various problems in civil works planning, risk assessment, hazard prediction, and spatial modeling. This research evaluated the accuracy of DEM generation produced from images obtained from unmanned aerial vehicles (UAV) in hovering mode (DEMf) and with eight Geodetic Control Points (DEMGCP), contrasted with the DEM generated from data from a Total Station - ST (DEMST) on the Bocanegra - Chachapoyas road, Peru. The photogrammetric images were processed with a spatial resolution of 50 cm. The mean, median, mode, mode, range, and standard deviation parameters were compared between DEMf, DEMGCP, and DEMST. In addition, statistical tests such as t-Student (α = 0.05) were applied for the difference of DEMs. The results showed a higher accuracy of the DEMGCP-derived vertical axis concerning DEMf with a combined error of 69.1 cm and an R2 of 0.999986; this is due to the use of GCPs to rectify the position on all axes of the generated DEM. Indeed, the use of UAVs is of great applicability in the generation of DEMs. They complement the data from a total station for the formulation, development, and implementation of civil works projects.

ABSTRACT Collection, processing and provision of comprehensive geometric information of forest roads is decisive for its technical classification to facilitate sustainable timber supply chains. An automized classification system based on the mobile proximal sensor platform RoadSens was developed, applied and validated through a case study approach in Eastern Norway. Six sample roads of various vegetation stages were surveyed through RoadSens and complemented through sampled total station measurements for validation purposes. The determined geometric parameters road slope, curvature and width were used for technical classification following the national forest road standard. Road width was identified as the main constraint in meeting the standard, resulting in a general downgrading of the sampled roads according to its technical class. The results showed a root mean square error (RMSE) ranging from ±0.53 to 1.50 m (12–33%) depending on the road and vegetation stage compared to the validation data. Despite these accuracy constraints, the application case study already indicates a general need for improvement of road data acquisition and updating of associated databases. The study underscores that, despite the challenges and limitations, there is a clear need for an automated sensing and classification system, which offers a cost-effective alternative to manual surveying and requires less specialized expertise.

The coastal area of Tanah Kuning, Bulungan Regency, is planned as part of the development of the Tanah Kuning-Mangkupadi Industrial Estate and International Port (KIPI) in North Kalimantan Province. This study aims to analyze port land readiness based on topographic conditions to inform technical planning for port development. The study offers an approach to evaluating land readiness by integrating terrestrial surveying using a Total Station with aerial photogrammetry using a UAV drone. The data were processed into a Digital Terrain Model (DTM) and contour maps for slope and land elevation analysis. The assessment was conducted descriptively and evaluatively, in accordance with KP.227/DJPL/2019, taking into account slope conditions, protection against the highest high-water level, and existing land use. The results show that location 1 has the most stable conditions, with dominant slopes of 0-25%, elevations above the highest high-water level, and no active settlements. Therefore, it is classified as suitable. In contrast, Locations 2 and 3 are classified as unsuitable due to the dominance of steep to very steep slopes and socio-land constraints.

Elevation measurement is fundamental to engineering construction and the acquisition of geospatial information, with its accuracy directly impacting project quality and safety. However, traditional high-precision leveling often suffers from low efficiency and operational difficulties in complex terrain. To address this, this study proposes a trigonometric leveling method using a high-precision total station based on the intermediate setup approach. By conducting multiple observation sets of distance and vertical angle, and combining them with trigonometric geometric relationships, elevation differences between control points were calculated. Measurements were carried out across seven control points located in topographically challenging areas. The results demonstrate that when the vertical angle does not exceed 30°, the difference in sight distances is less than 0.5 m, and the sight distance is within 600 m, the accuracy of this method meets the requirements for second-order leveling. This study technically verifies the feasibility of replacing second-order leveling with trigonometric leveling in complex terrain areas. On a practical level, it offers an efficient technical approach for surveying operations, contributing to significantly improved operational efficiency and reduced costs, thereby holding broad potential for application.

To address the difficulties in accurately capturing the characteristic changes of track irregularities in real - time and the limited ability to process complex mixed vibration signals, this study proposes an online Detection of Railway Track Irregularities via JADE-Based Blind Source Separation and MEMS Accelerometry. The system consists of a lower computer and an upper computer with ADXL345 three-axis acceleration sensor as the core. Real time track vibration signals are collected through optimized IIC bus protocol, and the blind source separation algorithm based on JADE is executed by STM32F103ZET6 microprocessor. By jointly diagonalizing the mixed vibration signal through a fourth-order cumulative matrix, the track roughness feature components in the mixed vibration signal are effectively decoupled, achieving accurate detection of railway track roughness. The detection results are converted into USB signals through RS-232 serial port and CH340G chip, and uploaded to the upper computer. The upper computer platform visualizes the type, location, and severity of track roughness faults. At the same time, a dual level power management and anti reverse protection are designed to ensure the reliability of the railway environment. To verify system performance, 8 monitoring points were set up on a 30 kilometer actual operating line, and multiple sets of vibration data were continuously collected at a sampling frequency of 10240 Hz at train speeds of 60-80 km/h. Establish the ground truth value of faults through high-precision track inspection vehicles and total station measurements, and compare it with HybridGAN method and data mining method. The experimental results show that this system can achieve an average positioning error of ≤ 1.8 mm, a fault type recognition accuracy of ≥ 96%, and an average detection time of ≤ 90 ms at a speed of 60 km/h. At a speed of 80 km/h, it still maintains an error of ≤ 2.2 mm and a recognition accuracy of ≥ 90%, with better performance than the two comparison methods. The upper computer of the system has the function of visualizing fault types, locations, and degrees, and integrates dual level power management and anti reverse protection, which is suitable for complex railway environments. This system provides a feasible solution for real-time monitoring of track status with high accuracy and low latency.

Accurate 3D documentation of large and complex structures is essential for long-term stability assessment, structural monitoring, and conservation planning, particularly for heritage sites exposed to environmental and anthropogenic threats. This study develops an integrated workflow combining Terrestrial Laser Scanning (TLS), Global Navigation Satellite System (GNSS), and Total Station geodetic control for large-scale, high-precision documentation. The approach was implemented at the Saqqara archaeological zone, a UNESCO World Heritage Site facing significant deterioration risks, to document four major pyramids: Djoser, Unas, Teti, and Userkaf. More than 2.1 billion georeferenced points were acquired from 16 scan positions with sub-centimeter registration errors and overall geometric accuracy better than ±1 cm. From these datasets, detailed mesh models, orthoimages, Digital Elevation Models (DEMs), contour maps, and 2D plans were derived. These enabled quantitative analyses of height loss and volumetric change, indicating severe structural degradation in Unas (~53%), Teti (~66%), and Userkaf (~63%), as well as localized deformations such as 4.2 cm displacement at Teti’s south flank. The degradation results from environmental factors and anthropogenic influences. Beyond this case study, the workflow proves that integrated TLS documentation can be applied to large and complex structures, supporting deformation monitoring, stability assessment, and digital twin development.

Aiming at the rain protection problem of outdoor precision instruments such as total stations, this paper designs an environmentally adaptive intelligent protection system based on D-S evidence fusion and state machine control. In terms of mechanical structure, the protective cover is designed as a sinking type, which not only improves the safety of the equipment but also avoids the shielding problem of the working surface compared with the traditional upward-lifting structure. The system collects data from multi-source meteorological sensors and uses D-S evidence theory for fusion decision-making. To alleviate decision conflicts in high-conflict scenarios, a conflict-guided dynamic weight adjustment strategy is introduced. Combined with a dual-layer finite state machine, the system realizes coordinated control between environmental perception and protection actions and can activate protection within 30 s under severe weather. Simulation results show that the improved method increases the response speed by 41.9-62.5% compared with traditional D-S fusion in weather transition conditions. In a 28-day field test, the system achieves a daily protection success rate of 96.4% and 100% reliability in critical weather transitions. The proposed system can provide reliable support for the all-weather safe operation of field precision measurement equipment.

ABSTRACT Displacement is a common occurrence in large‐span steel structures; however, the lack of standardized and targeted statistical analysis methods often leads to neglecting the characteristics of displacement and its variations, making it challenging to identify potential structural damage risks. This paper proposes a standardized data processing method using time‐series models of measured column displacement data for safety evaluation. The group of steel structures consists of 17 large‐span exhibition halls, and the column displacement has been continuously measured for a period of 14 years, resulting in an extensive dataset. A 12‐parameter tuple is introduced to store column information, including the displacement measured using prism‐free total stations. Modeling with a linear time‐varying model was applied to analyze the time series, establishing a correlation between statistical parameters and the nonlinear engagement mechanisms of the column nodes. By integrating various statistical methods, parameters highly relevant to the risk level of the columns were extracted, and the risk index was calculated to provide guidance for maintenance and management. The probability density distributions of the displacement over each 5 years indicate that the displacement is currently safe and controllable. Data analysis reveals that the displacement of the indoor columns along the long axis of the halls is not significant, while the displacement of the outdoor columns is very large. Correlation analysis indicates a relationship between glass curtain cracking and column displacement. The improved structural wind‐resistance bracing system has effectively reduced column displacement. The research highlights the importance of establishing a long‐term displacement database for structural safety evaluation and maintenance.

This study proposes a four-dimensional Historical Building Defect Information Modeling (HBDIM) framework designed to support the documentation, diagnosis, and conservation of deteriorated historic stucco elements. The framework integrates multi-source digital documentation techniques, including terrestrial laser scanning (TLS), high-resolution photogrammetry, and automated total station measurements with laboratory-based material diagnostics to create a unified digital environment for defect detection and conservation assessment. The approach was applied to the Baron Empain Palace in Egypt as a representative case study of complex architectural heritage affected by material deterioration. Within the HBDIM workflow, point cloud processing and defect-oriented information modeling were used to identify and spatially localize deterioration features such as cracking, erosion, and material loss. Laboratory investigations—including computed tomography (CT), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray fluorescence (XRF)—were conducted to evaluate the effectiveness of calcium hydroxide nanoparticle consolidation treatments and to relate microstructural material behavior to spatially mapped defects within the digital model. Mechanical testing demonstrated a significant improvement in material performance, with treated stucco samples exhibiting an average compressive strength increase of approximately 69.06% compared to untreated specimens. The results demonstrate that integrating digital documentation, defect-oriented modeling, and material diagnostics within a four-dimensional framework provides a robust platform for linking geometric deterioration patterns with material-level conservation performance. By embedding diagnostic data and treatment outcomes within a temporally structured digital model, the HBDIM approach supports preventive conservation strategies, long-term monitoring, and data-driven decision-making in sustainable heritage management.

As urban metro tunnels enter the middle-to-late operational stages, water leakage becomes a critical issue threatening structural safety and operational efficiency. Addressing challenges such as the short maintenance windows for operational tunnels and the unclear coupling mechanisms between seepage and structural interactions, this study systematically investigates leakage evolution mechanisms and rapid remediation methods for shield tunnel-ring beam joints in water-rich strata. Firstly, high-precision radar and total station instruments were employed to acquire leakage source data and structural deformation data. Subsequently, a seepage-structure coupling model was established using ABAQUS, revealing critical thresholds including the surface load critical effect (1.44 MPa) and strength degradation of the segment (0.8 times the original strength). Finally, an innovatively developed modified epoxy resin mortar material was applied for grouting remediation, demonstrating significant repair efficacy. The research achieves the first quantification of key leakage evolution parameters and establishes an integrated system encompassing leakage detection, mechanism analysis, and rapid remediation. These findings offer useful insights and a preliminary technical reference for leakage detection, mechanism understanding, and remediation in operational tunnels.

Precise track measurement of the geometric state of high-speed railways is a prerequisite for their smooth and safe operation. Current track inspection trolleys, which integrate only an inertial navigation system (INS) and a total station (TS), rely entirely on the track control network (CPIII) deployed along the track when calibrating their absolute location to avoid INS errors. Due to the high dependency on the surrounding CPIII points, this method faces severe challenges in terms of operational efficiency and cost control. To address this issue, this study utilizes the fast and precise positioning capability of the Chinese Beidou System (BDS) and proposes a novel adaptive location calibration approach using tightly integrated BDS/TS data. Using the Kalman filtering framework, this approach integrates BDS observations with the TS distance measurements in the observation domain, and the number of CPIII points to be observed is adaptively reduced according to the surrounding environments. Thus, the absolute location of track inspection trolleys can be quickly and accurately calibrated without INS data, greatly reducing dependency on CPIII points. Experiments were conducted under two typical scenarios: open-sky and blocked BDS signals. The results demonstrate that, under open-sky scenarios, the adopted BDS-only solution achieves positioning errors of less than 1.0 cm in the north, east, and up directions within 5 min, completely getting rid of the reliance on the control network, while in obstructed scenarios, where the BDS-only solution fails to converge at the 1 cm level within 5 min, the tightly integrated BDS/TS approach, combined with CPIII data, enables fast convergence in the northward and eastward, with positioning errors of less than 1 cm. The proposed approach provides a novel location calibration scheme in the track geometric states measurement under different environments, effectively reducing the dependence of track measurement operations on CPIII points and significantly enhancing measurement efficiency and flexibility.

The RTK (Real-Time Kinematic) method is an effective method for fast and sufficiently accurate positioning using Global Navigation Satellite System (GNSS) technology. The combination of a GNSS receiver with other sensors brings expanded use. In this article, we test a laser RTK method which is the unusual combination of a GNSS receiver with an Inertial Measurement Unit (IMU) and a laser rangefinder, as implemented in the new HiTarget V6001 receiver. We compare the accuracy of determining the spatial position of hard-to-reach points when targeting buildings and other objects. The reference was the determination of the position using the spatial polar method with a precise total station. Its position was determined as a free station relative to three points determined by the GNSS RTK method. The tests showed that the HiTarget V6001 receiver determines the spatial position with an accuracy corresponding to the specifications. Only 2 points out of a total of 35 were determined with an error exceeding the expected accuracy. A comparison of the results revealed a systematic vertical bias of 17 mm. The HiTarget V6001 receiver is fast and economical for determining position or staking out dozens of points. Positioning is easier and faster than with a combination of GNSS receiver and a total station. The spatial position accuracy of 50 mm on average in the horizontal components and 87 mm in the vertical component (for an average slant distance of 18 m) is sufficient for most mapping and staking-out work. More than 80% of the accuracy is attributable to the polar method implemented using a laser rangefinder and IMU. The accuracy pf RTK positioning accounts for only about 20% of the total accuracy.

Digital elevation models (DEMs) play a key role in extracting morphometric factors like fill sink, flow accumulation, profile, flow width, slope, plan curvature, aspect, and total catchment to estimate the Topographic Wetness Index (TWI) that provides key information for modelling and predicting hazards related to mass movement or landsliding. The range and accuracy of information, including the topographic feature, can influence the quality of results, significantly impacting the severity and likelihood of occurrence of mass movement. Therefore, it plays a crucial role, especially in mountainous regions. This research aims to investigate, evaluate, and identify the optimal downscaling methodology for DEMs and assess its impact on DEMs at different spatial resolutions. Morphometric factors derived from the DEM were examined using six distinct methodologies: kriging, nearest neighbor, majority, bilinear, bi-cubic, and the Hopfield Neural Network (HNN). Three geospatial databases containing a 20 m, 12.50 m, and 1.50 m resolution DEM were used for analysis. Six downscaled topographic maps were generated: kriging, nearest neighbor, majority, bilinear, bi-cubic, and the HNN. Results validated from field elevation survey points have an accuracy of 1.50 m from total stations and the Global Positioning System (GPS). By predicted actual values at corresponding locations, field survey points named GPS, and total station data having vertical accuracy of 1.50 m, were used as a standalone reference source to validate the downscaled results. Consequently, we strongly endorse downscaling DEM employing the HNN technique to obtain the most precise morphometric parameters for fill sink, flow accumulation, profile, flow width slope, plan curvature, aspect, and total catchment to assess TWI maps. The result shows RMSE accuracy improved accuracy approximately 25% to 75% and 93%, respectively, when progressing from low to medium resolution (30, 20, and 12.50 m) DEMs. It is concluded that all the mentioned techniques, Bi-cubic, HNN, Nearest Neighbor, Majority, Bilinear, and Kriging, can improve the overall accuracy of DEM. However, Bi-cubic and HNN methods demonstrate superior accuracy relative to the Bilinear, Nearest Neighbor, Majority, and Kriging methods. This study attempts to address a gap in past research by evaluating and selecting the most effective downscaling approaches for DEMs and testing their accuracy to enhance topographical features at various spatial resolutions in mountainous regions.

“High sampling rate” GPS/GNSS (Global Positioning System/Global Navigation Satellite System) and RTS (Robotic Total Station) measurements were used to extract the vibration functions of, respectively, ca. thirty buildings hosting permanent GPS stations and the ties of four masonry arches of a church. The logarithmic decrement, frequency response, frequency domain decomposition and spectral response methods were applied to the measurements to estimate the damping ratio ζ. The methods’ costs, times, calculation codes and application to the case studies were compared to evaluate whether “high sampling rate” GPS and RTS are suitable for estimation of the damping ratio ζof a building or a tie.

Measuring coal volume in the Stock ROM area played an important role in production control and mining evaluation. This research was conducted in the Stock ROM area at PT. Victor Dua Tiga Mega, where volume measurements were generally carried out using Total Station (TS) as the main method, howefer the use of Unmanned Aerial Vehicles (UAV) has begun to be implemented as a more efficient alternative. This study aims to compare the results of Stock ROM coal volume calculations using the Total Station and UAV methods. The research method was carried out by collecting data in the field, processing the digital Elevation Model (DEM), and calculating the volume using the Cut and Fill method. The results of the study the difference in volume between the two methods, where the UAV measurement results tend to be greater than those of the Total Station. The difference in Fine Coal volume was 724,15 m3 or 16,74% and Raw Coal volume of 9.335,98 m3 or 8,03%. Based on a comparison with weighing data, measurements using the Total Station provided results that were closer to the actual conditions in the field.