Search Results (436)

Automated three-dimensional (3D) mapping with a survey-grade terrestrial laser scanner (TLS) is the canonical instance of robotic stop-and-scan light detection and ranging (LiDAR) mapping: the mobile platform must remain stationary for minutes per scan, so the environment must be covered with as few scans as possible. Where to stop hinges on a coverage model predicting what a scan pose will observe. The conventional isotropic-disk model counts every free cell within sensing range as covered—including cells behind walls—and, therefore, skips scans that genuine observation requires. We replace the disk with a ray-cast visibility region computed on the robot’s live two-dimensional (2D) occupancy map, admitting only cells in direct line of sight; define a line-of-sight (LOS) coverage metric over mapped free space and drive a marginal-gain scan/skip rule embedded in frontier exploration. The planner thus performs 2D scan-station placement for subsequent 3D mapping. In a controlled paired ablation in simulation, the visibility rule raises LOS coverage from about 77% to 84–85% for one to two additional scans, and it also outperforms a disk baseline governed by the identical marginal-gain rule, isolating the coverage model itself as the decisive factor. In a fully autonomous on-hardware comparison in an industrial test room, with each rule driving a mobile platform carrying a Leica BLK360 G1, the visibility rule raised LOS coverage from 77.6% to 92.1%; every visibility run’s scans registered offline at survey grade (4–5 mm bundle error, 84–88% overlap), whereas disk runs yielded at best a single-link network and once a single unregistrable scan. The results indicate that, for the room-scale indoor environments studied, occlusion-aware visibility is a sounder basis than Euclidean proximity for stop-and-scan placement. Full article

Current micro/nanosystems mainly rely on a planar fabrication framework, where structures are built layer-by-layer on flat surfaces. This conventional approach leaves vertical sidewalls underutilized, posing geometric limits in packaging density, three-dimensional (3D) interconnects, and multi-surface functionalization. To overcome these constraints, sidewall patterning has emerged as a promising strategy, enabling 3D integrated circuits, templates for directed nanostructure synthesis, and microfluidic drag reduction. Nevertheless, traditional photolithography and non-photolithographic techniques face challenges when applied to vertical or curved 3D surfaces. Unidirectional radiation and restricted focal depths prevent high-fidelity pattern transfer, even when using soft lithography, scanning probes, or nanoimprinting. To address these geometric and mechanical barriers, our group has developed several approaches for patterning the sidewalls of microsystems, which are the primary focus of this review. Building upon our approaches, this review further surveys related sidewall-patterning strategies, including micro-transfer printing, multi-stimuli-responsive mechanics, block copolymer self-assembly, two-photon polymerization, and laser-induced forward transfer. Collectively, these techniques expand the capabilities of sidewall engineering and provide valuable insights into next-generation 3D micro- and nanomanufacturing. Full article

This study proposes an integrated approach to the digital documentation and system-level analysis of heritage buildings, combining LiDAR-based data acquisition, H-BIM modelling and DEMATEL analysis. The novelty of the study lies in combining a Scan-to-BIM workflow with DEMATEL-based system analysis in order to identify causal and dependent stages in the heritage building digitisation process. The research was carried out on two heritage buildings in south-eastern Poland: the Church of St Onuphrius in Posada Rybotycka and a wooden manor house from Brzeziny preserved in the ethnographic park in Kolbuszowa. Terrestrial laser scanning was used to acquire high-resolution point clouds of the buildings, which then provided the basis for developing parametric H-BIM models within a Scan-to-BIM workflow. For the church case study, the geometric accuracy of the Scan-to-BIM output was verified by comparing measurements derived from the point cloud with traditional surveying data. The results confirmed the suitability of Scan-to-BIM for heritage documentation, with an average absolute deviation of approximately 7 mm and a maximum deviation not exceeding 31 mm. DEMATEL analysis was used to examine cause–effect relationships between seven stages of the digitisation process and to determine which stages have the greatest influence on subsequent activities. Preliminary assessment, LiDAR scanning and H-BIM modelling were identified as causal stages, while validation, IoT integration, AI-based predictive analysis and digital twin development were classified as effect stages. The study also outlines how H-BIM models may be extended through IoT sensors and AI-based analytics as a basis for future digital twin development. Full article

The transition toward urban-scale digital twin and smart city management requires survey-grade 3D basemaps, yet conventional documentation remains time-consuming and prone to inaccuracies. While Terrestrial Laser Scanning (TLS) offers rapid 3D acquisition, capturing complex, GNSS-denied multi-story interiors frequently causes cumulative registration errors and isolated indoor–outdoor data silos. To address this, this study proposes a comprehensive typology-agnostic framework for developing high-fidelity digital twin basemaps. Treating the building as a unified spatial network, the methodology systematically mitigates error propagation through strategic linkage planning, rigid shell-first registration, continuous vertical core anchoring (via stairwells), and adaptive multi-source data fusion. Implemented on an adaptively reused heritage building, the developed basemap achieved an absolute georeferencing accuracy of 30.0 mm (RMSE) against an independent total station control network, alongside a mean relative error of 3.11 mm. Comparative analysis against legacy 2D CAD floor plans revealed simplified geometric representations and categorical dimensional deviations of up to 29.8 cm. Demonstrating its practical utility, the point cloud-centric geometric hub avoids forced geometric idealization, successfully supporting direct immersive visualization, architectural slicing (floor plans, sections, elevations), and multi-LOD algorithmic planar segmentation. This spatially constrained acquisition strategy bypasses legacy limitations, delivering a mathematically verified 3D reality capture essential for smart facility management, heritage conservation, and downstream semantic intelligence. Full article

Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR, has become an essential close-range remote sensing technique for high-precision engineering surveying, deformation monitoring, industrial inspection, and cultural heritage documentation. The geometric reliability of TLS point clouds strongly depends on the effective compensation of instrumental systematic errors through in situ self-calibration. However, conventional target-based self-calibration often suffers from strong coupling between calibration parameters and exterior orientation parameters, whereas recently developed coplanarity-constrained formulations generally require highly redundant target networks, limiting their field efficiency. To address this limitation, this study proposes a variance inflation factor (VIF)-driven minimal network design strategy for efficient in situ geometric self-calibration of TLS systems. Unlike the commonly used geometric dilution of precision, VIF provides a dimensionless statistical alternative that effectively resolves the dimensional inconsistency inherent in traditional GDOP when handling mixed angular and distance parameters. A differential evolution algorithm is employed to search for hybrid calibration networks that minimize parameter coupling while preserving the physical interpretability of the National Institute of Standards and Technology (NIST) 10-parameter instrumental error model. Five digital twin simulation experiments and a physical validation experiment using a Faro Focus 350 scanner were conducted to evaluate the proposed method. The results show that the optimized network substantially reduces the number of required targets while maintaining high calibration accuracy. The final configuration, which combines VIF-optimized target placement with a dual-station height-difference constraint, reduces the condition number of the normal equations to below 60 and yields a mean system VIF close to 10. The maximum parameter correlation coefficient among the key calibration parameters is constrained to approximately 0.75, indicating near-optimal parameter decoupling under the limited field-of-view geometry of the instrument. These findings demonstrate that the proposed VIF-driven network design provides a highly effective strategy for field-efficient TLS self-calibration and improves the geometric reliability of terrestrial LiDAR point clouds in high-precision remote sensing applications. Full article

Cultural heritage faces accelerating threats from armed conflict and climate-induced disasters, generating emergency scenarios in which the window for documentation may be measured in hours. Traditional high-fidelity workflows such as Terrestrial Laser Scanning and survey-grade Structure-from-Motion campaign can be operationally incompatible with these constraints due to costly hardware and specialist-team requirements. Emergency image acquisition can be undertaken with consumer-grade equipment, while the same photographic dataset can subsequently support both an SfM-MVS mesh for metric and geometric analysis and a 3D Gaussian Splatting (3DGS) representation for view-dependent visual interpretation. This paper evaluates an accessible, entirely graphical workflow for producing these complementary outputs, with particular attention paid to the 3DGS stage and without requiring programming or command-line expertise. We develop a theoretical case drawing on Brandinian restoration theory and the international conservation framework, introducing the concept of informational amnesia, the irreversible loss of a site’s documentary record, as a distinct and undertheorized category of heritage failure. We further argue that the democratization of 3DGS documentation cannot be measured by licensing cost alone: a tool distributed as open-source code but requiring command-line expertise presents an effective accessibility barrier functionally equivalent to a commercial paywall. True accessibility requires installable, graphical software operable by non-specialist practitioners in the field. We present a proof-of-concept application of this accessible pipeline, namely DJI Mini 5 Pro, RealityScan for photogrammetric reconstruction, and Lichtfeld Studio for 3DGS generation, to the Fontana d’Ercole at the Reggia di Venaria Reale (UNESCO World Heritage, Turin, Italy). Visual results show photorealistic, navigable 3DGS surrogates of architecturally complex heritage assets achievable by non-specialist operators within hours of acquisition and establish the empirical foundation for future quantitative evaluation. Full article

The accurate estimation of forest aboveground biomass (AGB) typically relies on extensive field surveys, which are highly time-consuming and cost-prohibitive. While unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) provides ultra-high point densities capable of reliable individual-tree analysis, its limited flight coverage restricts large-scale applications. Conversely, regional airborne laser scanning (ALS) offers broad spatial coverage, but its relatively low point cloud density makes individual-tree level analysis unreliable. To bridge this scale and data gap, this study develops a scale-consistent framework that integrates UAV-LiDAR, three-dimensional simulation, multisource remote sensing, and machine learning for Chinese fir (Cunninghamia lanceolata) plantation AGB estimation. High-density UAV-LiDAR data were first used to construct individual-tree AGB models, and the predicted tree-level biomass was aggregated to generate spatially representative “agent plots” for stand-scale modeling. A three-dimensional (3D) radiative transfer simulation framework was further employed to reproduce airborne LiDAR observations under different point densities, enabling the evaluation of structural information loss caused by LiDAR sparsity. Structural features derived from simulated LiDAR and spectral information from Sentinel-2 imagery were integrated using the Tabular Prior-data Fitted Network (TabPFN). Model reliability was assessed through 10-fold spatial block cross-validation and Monte Carlo simulations, which quantified spatial generalization and uncertainty propagation from individual-tree estimation to stand-level prediction. Feature interpretation using SHapley Additive exPlanations (SHAP) revealed that the LiDAR-derived vertical canopy structure provided the primary constraints for biomass estimation, whereas Sentinel-2 shortwave infrared features supplied complementary information related to canopy conditions. The optimal TabPFN model achieved a stand-level accuracy of R² = 0.88 and RMSE = 9.23 Mg·ha⁻¹ using LiDAR combined with Sentinel-2 data. Uncertainty analysis further demonstrated the robustness of the proposed framework under propagated errors, highlighting its potential for scalable and reliable forest biomass estimation in data-limited subtropical ecosystems. Full article

To examine the deformation behavior and failure mechanisms of large-scale mine chutes under complex service conditions, we performed multiple C-ALS three-dimensional laser scanning surveys on the 1#, 2# and 3# chutes at an iron mine in Anhui Province. The results showed that all three chutes had severe non-uniform expansion, with maximum diameters of 12.2 m, 14.8 m and 16.9 m, respectively. The maximum annual wear rates during the detection period were 2.4 m, 6.0 m and 2.5 m, respectively. The failure of the chutes was mainly caused by local block collapse drops, influenced by the joints and fissures of the surrounding rock, and the wear showed significant discontinuous and non-uniform characteristics. Based on the detection data, a three-peak Gaussian model is established to describe the nonlinear distribution of the expansion along depth, with peak centers located at −462 m, −497 m and −520 m. By comparing linear and exponential models, it is determined that the expansion exhibits a linear evolution trend with time. Based on the maximum diameter and annual wear rate, we estimate the remaining safe service life of each chute, and provide the confidence interval for the expansion value at a 90% confidence level (such as [1.31 m, 4.83 m] at −520 m). Research shows that high-precision 3D laser scanning can effectively reveal the deformation and evolution laws inside the chute. It is recommended to immediately take reinforcement measures for chute 2 and establish a dynamic monitoring system for the common weak zone between −480 m and −520 m. Research on the evolution of surrounding rock fractures should be carried out to provide a scientific basis for chute life assessment and risk control. Full article

Accurate estimation of forest structural attributes over operational scales remains challenging because unmanned laser scanning (ULS) provides extensive spatial coverage but limited representation of internal stem structure, whereas terrestrial and mobile laser scanning (TLS/MLS) provide detailed stem measurements over relatively small areas. This study investigates a calibration-transfer framework in which small areas of terrestrial or fused LiDAR are used to improve diameter at breast height (DBH) estimation across much larger regions surveyed only by ULS. ULS, TLS, MLS and fused laser scanning (FLS) datasets were analysed for radiata pine and eucalyptus plantations. TreeLS-derived DBH measurements from terrestrial and fused point clouds were used as reference data to evaluate several distribution-aware and voxel-based imputation approaches for correcting regression-derived ULS estimates. Across the study sites, the best-performing imputation methods reduced stand-level mean DBH differences by as much as 95% relative to the uncorrected ULS regression estimates, resulting in substantially improved agreement with field-observed stand means while simultaneously producing DBH distributions that more closely matched the corresponding TreeLS-derived reference distributions. Voxel-based imputation performed particularly well for radiata pine and remained competitive for eucalyptus, while several distribution-based approaches achieved comparable or better performance in particular stands. These findings demonstrate the potential for transferring information from relatively small terrestrial LiDAR calibration areas to larger ULS-only acquisitions, improving stand-level DBH distribution estimates without requiring complete terrestrial coverage. Because validation was performed using stand-level field summary statistics rather than matched individual trees, the reported performance should be interpreted as demonstrating the potential of the approach under the conditions evaluated rather than universal individual-tree accuracy. Full article

This study presents a data-driven approach, MLS-CAPS (mobile laser scanning completeness analysis for path spacing), for defining optimal path spacing (walking path) for backpack or handheld MLS surveys in ecological applications. By using an initial high-density pilot scan of the site, captured with the same MLS system intended for subsequent full surveys, MLS-CAPS quantifies how voxel occupancy completeness, digital terrain model accuracy, and canopy height model accuracy vary with lateral distance from individual walking trajectories. This provides an objective basis for translating local vegetation structure into path spacing recommendations for subsequent MLS surveys. Case study results showed that structural complexity strongly influences the rate of decay, with denser and more complex vegetation requiring closer path spacing to maintain data completeness. While this meets expectations, what has previously been lacking is a way to quantify it in a manner that directly supports data acquisition decision making. MLS-CAPS, provided as a Python tool, allows users to define thresholds aligned with their metrics of interest, recognising that no single spacing is universally sufficient across all structural attributes. The framework therefore enables MLS operators to plan surveys that balance efficiency with accuracy, while maintaining transparency in the trade-offs between path spacing and completeness. By formalising what has previously been a trial-and-error process, this method offers a practical tool to support ecological applications of MLS across diverse environments. Full article

Storm Kristin affected central Portugal on 28 January 2026, producing exceptional wind gusts and widespread disruption across the natural, infrastructural and built environments. This paper presents the results of a post-disaster reconnaissance mission carried out in the municipalities of Leiria and Marinha Grande, two of the most affected areas. The methodology combined ground-based visual inspection, photographic records, unmanned aerial vehicle surveys, terrestrial laser scanning, and information provided by local authorities and infrastructure managers. The results show that the spatial distribution and severity of the damage were consistent with the occurrence of very intense localised gusts associated with a sting jet mechanism. Extensive damage was observed in vegetation, transport networks, electrical and telecommunications infrastructure, public spaces and different building typologies. Across the building stock, roofing systems were the most vulnerable components, particularly lightweight panels, ceramic tiles, ridge elements and poorly anchored envelope systems. Industrial and public buildings showed the most severe failures, including extensive roof loss, deformation of structural and envelope components, and local collapse, while residential buildings generally presented minor-to-moderate damage. Heritage buildings showed specific vulnerabilities related to cumulative degradation, exposed architectural elements and the consequences of local damage for historic fabric. The findings highlight the need for improved wind-risk preparedness, systematic post-event documentation, enhanced roof and cladding anchorage, preventive maintenance, and further investigation of the relationship between recorded wind speeds, observed damage patterns and current wind-design assumptions. Full article

Sustainable forest management requires inventory workflows that provide spatially explicit structural information while retaining field-based calibration and uncertainty control. This case study evaluated a field-calibrated UAV LiDAR workflow for individual-tree inventory in middle-aged and near-mature larch plantations in Chuanying District, Jilin City, China. UAV laser scanning point-clouds were integrated with six 30 m × 30 m field plots to assess three individual-tree extraction algorithms: point-cloud segmentation (PCS), marker-controlled region growing (MCRG), and region-based hierarchical cross-section analysis (RHCSA). Algorithm performance was evaluated using plot-level recall, precision, F-score, localization RMSE, tree-height and crown-width accuracy, bootstrap confidence intervals, exploratory Wilcoxon signed-rank comparisons, and leave-one-plot-out stability checks. MCRG provided the most balanced numerical performance under the tested configuration, with a mean F-score of 0.845, compared with 0.808 for PCS and 0.827 for RHCSA. However, the MCRG-RHCSA paired difference was not robust across the six plots, and the analysis should be interpreted as a dataset-specific workflow comparison rather than a universal algorithm ranking. Tree height was estimated with comparatively high accuracy, whereas crown-width estimation remained weak, indicating that vertical canopy structure was more reliable than lateral crown delineation. After calibration assessment, the workflow was applied to 157.47 ha of UAV LiDAR survey areas and generated 219,996 algorithm-based detections. These outputs are best interpreted as a spatial decision-support layer for compartment updating, density screening, and field-inspection prioritization, not as an independently verified wall-to-wall stem census. Full article

The purpose of this study is to examine the importance of architectural details and artefacts as cultural markers within the interior spaces of adaptively reused heritage buildings in Sofia’s historic urban centre. A comparative study was performed on three significant early twentieth-century residential buildings: the house linked to the former ‘Excelsior’/‘Asen Zlatarov’ cinema complex at Blvd ‘Christo Botev’ N85, the former residence at Blvd ‘Christo Botev’ N75, and the building at Blvd ‘Stamboliyski’ N36—the latter two have both been transformed into hotels. The research employs an interdisciplinary methodology combining archival research, field surveys, photographic documentation, 3D laser scanning, and comparative stylistic analysis. The study’s principal contribution is the development of a transferable analytical framework for classifying interior details by function (constructive or decorative), location (enclosing surfaces, fixed furnishings, vertical circulation), and material—a framework not previously applied to this building stock in the Bulgarian context. The findings confirm that authentic interior details—including stair railings, flooring patterns, gypsum cornices, ornamental plasterwork, heating appliances, and joinery—function as reliable cultural artefacts and markers of historical period and stylistic identity. These details serve as evidence for dating, authenticating, and analysing the evolution of architectural style elements. Preserving and integrating these elements into adapted interiors strengthens architectural identity and supports the sustainable reuse of cultural heritage for contemporary purposes. Full article

Digital twins are increasingly adopted in geographic research as dynamic representations of environmental systems; however, their application to regulated river shorelines remains limited, particularly where bathymetric change, hydrological variability, and shoreline-state dynamics must be integrated within a single GIS-based framework. This study develops and validates a GIS-based spatiotemporal digital twin-oriented framework for the dam-affected shoreline downstream of the Włocławek Dam, Poland. The framework integrates four bathymetric surveys acquired in 2008–2011, water-level records, airborne laser scanning data, and three-dimensional hydrotechnical infrastructure within a unified geodatabase designed for dynamic shoreline-state reconstruction, multi-epoch analysis, and environmental monitoring. A key methodological element is the treatment of water level as a dynamic reference surface, enabling the automated delineation of inundation and exposure zones for observed and scenario-based hydrological conditions. The reconstructed bathymetric surfaces were organized as a multidimensional raster dataset with time as an explicit analytical dimension, supporting repeatable change detection, cross-sectional interpretation, and temporal trend analysis. To extend the framework beyond purely retrospective analysis, a near-real-time hydrological updating component was implemented through ingestion of operational water-level observations from the IMGW API into the geodatabase. Validation of the trend-based prediction for 2011 yielded R² = 0.967, RMSE = 0.44 m, MAE = 0.28 m, and bias = −0.06 m. The proposed framework provides a transferable geospatial basis for spatiotemporal modelling and monitoring of regulated river shoreline dynamics under changing hydrological conditions. Full article

With advances in computer vision and modern surveying technologies, intelligent inspection systems and automatic recognition methods are increasingly used in highway tunnel maintenance. However, existing mobile inspection methods still struggle to balance high-speed operation, fine-crack recognition, and comprehensive assessment of multiple defects. This study proposes an automatic recognition and quantitative assessment method for multiple visible defects in highway tunnels based on a vehicle-mounted multisensor inspection system. The system integrates high-resolution imaging, infrared illumination, 3D laser scanning, mileage positioning, and high-speed data storage, enabling continuous full-section data acquisition at speeds up to 80 km/h. A structural-feature-constrained mileage correction strategy is developed to reduce accumulated localization errors. For crack analysis, a multilevel framework combining two-stage CNN screening, cascaded segmentation, crack trajectory tracking, and subpixel edge extraction is established for crack recognition and 0.1 mm-level width measurement. Water leakage and spalling are extracted through visible–infrared image fusion and adaptive boundary refinement, while cross-sectional deformation is calculated using 3D tunnel axis reconstruction, point-cloud filtering, and cross-section fitting. Field tests and controlled experiments demonstrate that the system can rapidly identify, locate, and quantify multiple tunnel defects, providing a practical reference for intelligent tunnel inspection and maintenance. Full article