Search Results (627)

Accurate stem volume estimation is fundamental to forest resource inventory and carbon stock assessment. Traditional methods rely on destructive sampling, whereas terrestrial laser scanning (TLS) offers a non-destructive alternative. However, the accuracy of individual tree segmentation in structurally complex subtropical natural forests is constrained by crown overlap, species mixing, and vertical stratification. In this study, we developed a semantic voxel-based framework for individual tree segmentation and robust stem volume estimation from plot-level TLS point clouds. The method integrates 3D-CNN-based voxel semantic classification with bottom-up tree growth segmentation, followed by parameter extraction, taper equation fitting, and volume estimation using the sectional measurement method. Evaluation across 18 plots (1451 trees) in Guangxi, Southern China, demonstrated that the proposed method achieved an F-score of 0.881 for individual tree segmentation, significantly outperforming conventional CHM-based (0.533) and geometric voxel-based (0.794) approaches. Optimal taper equations were established for Chinese fir (Zeng Weisheng model, validation R² = 0.943) and Eucalyptus (Yan Ruohai model, validation R² = 0.987). TLS-based volume estimates yielded R² values of 0.94–0.97 and RMSE of 0.022–0.037 m³ per tree, with negligible systematic bias. These findings demonstrate that the proposed semantic voxel framework enables accurate and non-destructive stem volume estimation in complex subtropical forests, providing a practical technological pathway for modernizing forest inventory practices. 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

The Greater Cape Floristic Region (GCFR) in South Africa is a fire-prone biodiversity hotspot where high species richness, structural complexity, and small plant sizes (0.0001–4 m²) pose substantial challenges for remote sensing-based biodiversity assessment. Spectral similarity among species and the mismatch between plant size and sensor pixel dimensions limit the capacity of current and forthcoming spaceborne systems to resolve individual species and accurately detect plot-level diversity changes. We therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring. We constructed a three-dimensional virtual scene of post-fire fynbos communities in Grootbos Private Nature Reserve, integrating high-resolution imagery, terrestrial laser scanning (TLS), and structure-from-motion (SfM)-derived point clouds. Field measurements of mean diameter and percent cover were used to scale vegetation models and constrain species abundance. We distributed plant instances using a blue noise sampling algorithm, guided by density maps derived from unmanned aerial system (UAS) imagery. Species-specific optical properties were parameterized using field-measured reflectance data and the PROSPECT radiative transfer model, while terrain structure was derived from SfM-based digital terrain models. The integrated scene was used to simulate multispectral (DJI Mavic 3 MSI), hyperspectral (AVIRIS-NG), and light detection and ranging (LiDAR) observations. Agreement between simulated outputs were evaluated against corresponding field-acquired datasets using spectral signatures and vegetation indices. This framework enables systematic assessment of sensor specification effects on spectral biodiversity metrics and provides a pathway for evaluating theoretical limits of species discrimination across airborne and satellite platforms. Full article

This study presents a hybrid geodetic validation framework for assessing the geometric consistency of the axis of a steel lattice transmission tower determined from terrestrial laser scanning (TLS) data using an independently established tachymetric reference. Unlike previous investigations that focused on the influence of TLS scanner characteristics, registration strategies, or internal consistency of TLS-derived axes, the proposed approach introduces an external geodetic reference, enabling direct external assessment of TLS-based geometric axis estimation. The reference axis was determined at fourteen height levels, while the TLS axis was estimated from horizontal cross-sections of a point cloud acquired from multiple scanning stations and registered using a cloud-to-cloud method without control points. To enable direct comparison, both datasets were transformed into a common reference system using a seven-parameter Helmert transformation. The transformation was applied solely to remove differences between the independent local coordinate systems prior to the geometric comparison. Axis consistency was evaluated using residual vectors and three-dimensional distances between corresponding points. The mean deviation was 0.031 m, the RMS value was 0.033 m, and the maximum deviation reached 0.078 m. Larger discrepancies occurred predominantly in the upper sections of the structure, in a pattern consistent with the combined influence of TLS registration uncertainty, non-uniform point-cloud coverage, and local geometric conditions. A comparison of TLS axis estimators (centroid, LS-R regression, and PCA) showed that PCA produced an RMS value close to that of the centroid estimator, whereas LS-R produced a higher RMS value; the maximum deviation was lowest for the centroid estimator and highest for PCA. Regression analysis revealed a statistically significant linear trend in the X direction (p = 0.019), indicating residual systematic geometric drift after coordinate-system integration. The obtained discrepancies should be interpreted in the context of a rapid engineering TLS workflow performed without registration targets or a control network, rather than as the intrinsic accuracy of the TLS instrument itself. The proposed hybrid validation framework provides an objective quality-control methodology for evaluating TLS-derived geometric axes against independent geodetic observations and may support reliability assessment of TLS-based inventories and deformation monitoring of slender engineering structures. Full article

Masson pine (Pinus massoniana) is a core timber species in the subtropical regions of Southern China, and the precise monitoring of its growth status and phenotypic characteristics is crucial for forest resource management. To address the segmentation challenges caused by intertwined wood-leaf structures and severe occlusion, this study proposes PineSegNet, an end-to-end deep learning framework for fine-grained semantic segmentation of Masson pine point clouds. The framework adopts an encoder–decoder architecture. In the encoding stage, a Hierarchical Local–Global Aggregation (HLGA) module is introduced to capture multi-scale features through progressive downsampling. This design suppresses noise and enhances high-frequency geometric details of branches. In the decoding stage, a Boundary Refinement Unit (BRU) is designed to effectively curb feature diffusion during the interpolation process, significantly enhancing the clarity of category boundaries. Furthermore, this study develops a composite loss function with a dual-supervisory mechanism, namely the CE-Dice Composite Loss (CDC-Loss), to tackle semantic confusion caused by inter-class geometric similarity and the challenges of extreme sample distribution imbalance. Experimental results demonstrate that on both the self-collected dataset (GAOFENG) and the public dataset (FOR-instance), PineSegNet exhibits exceptional robustness and generalization capability, outperforming all evaluated semantic segmentation baselines under the unified experimental setting. This study provides a reliable technical solution for precision forest resource inventory and tree structural analysis within the framework of smart forestry. 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

The integration of advanced terrestrial laser scanning (TLS) techniques into building information modelling (BIM) workflows has significantly enhanced the digitization and preservation of cultural heritage sites. In this context, crowdsourcing can operate as a complementary upstream mechanism for enriching heritage inventories, supporting participatory mapping, collecting preliminary condition observations, and prioritizing assets for subsequent expert-led TLS acquisition, HBIM modelling, and structural assessment. This paper explores the application of TLS within BIM for the structural assessment and the long-term monitoring of historic structures. The reviewed studies show how high-resolution TLS data can support the development and updating of geometry-informed structural models, enabling detailed structural assessment and effective management strategies. The reviewed case studies demonstrate the practical benefits of integrating TLS with BIM, such as improved accuracy in structural assessments, enhanced data processing capabilities, and the creation of comprehensive digital twins. These advancements facilitate better-informed decision-making and targeted preventive measures, contributing to the resilience and preservation of cultural heritage assets. The results underscore the contribution of TLS–BIM/HBIM workflows to condition-informed documentation, structural assessment, conservation planning, and long-term heritage management. This research was supported by the ERA4CH project, which aims to protect European cities’ cultural heritage from earthquake risks through innovative monitoring and management tools. Full article

Terrestrial laser scanning (TLS) point clouds are increasingly used for monitoring steel grid structures, and accurate instance segmentation is central to their processing. In complex environments, segmenting junction regions is challenging owing to multi-member geometry and incomplete sampling. Existing approaches frequently depend on prior information such as design drawings or Building Information Modeling (BIM), which limits generality and offers few solutions when model priors are unavailable. A hierarchical spherical coordinate segmentation with dual-sphere center refinement method (HSC-DCR) is proposed for geometry-driven junction region instance segmentation. The method uses radial connectivity. A segmentation origin is first located via a grid search driven by directional convergence evaluation, and a spherical coordinate system is then established for initial angular domain clustering. Subsequently, topological correction is guided by multi-dimensional indicators, and instance refinement is achieved through dual-sphere center optimization. The process is geometry-driven and independent of design models. Experiments on a laser-scanned stadium point cloud, covering 22 junction-region types and 521 instances, achieve an F1-score of 0.948 and mIoU of 85.5% under an instance-matched evaluation protocol. The results show that HSC-DCR can reliably obtain node and member instances from TLS point clouds without relying on drawings or BIM, supporting TLS-based monitoring of steel grid structures. Full article

Indoor wall surface flatness is a mandatory acceptance item in building decoration work, but it is still commonly checked using a 2 m straightedge and a feeler gauge. This manual method samples only a limited number of locations, depends on inspector judgement, and provides no full-surface spatial record. This study proposes a terrestrial laser scanning (TLS)-based automated workflow for wall surface flatness inspection that remains consistent with the Chinese national standard GB 50210-2018. After point-cloud registration and wall segmentation, a least-squares best-fit plane is established for each wall, and the signed point-to-plane deviation field is computed. A virtual 2 m straightedge operator is then applied at multiple positions and orientations to reproduce the code-defined measurement in which the maximum gap under the straightedge is taken as the flatness reading. The method was validated against paired manual measurements on a reference wall, showing a mean difference of 0.00 mm, a standard deviation of 0.20 mm, an RMSE of 0.20 mm, and a correlation coefficient of 0.98, with identical accept/reject decisions at the 4 mm limit. A residential case study involving four inspection zones and 42 measurement points further demonstrated that the workflow can generate code-comparable flatness readings, full-surface deviation maps, and localized rework guidance. The overall pass rate was 92.9%, with all non-conforming points concentrated in the living room, and the on-site inspection time was approximately halved compared to manual checking. The results indicate that the proposed TLS-based virtual-straightedge method provides a practical, traceable, and standard-compliant alternative for automated wall surface flatness acceptance. Full article

Mining-induced surface cracks are direct indicators of ground damage caused by underground coal extraction, yet their accurate three-dimensional (3D) characterization remains challenging because most unmanned aerial vehicle (UAV)-based studies are limited to two-dimensional (2D) detection and planar parameter extraction. This study proposes a UAV–terrestrial laser scanning (TLS) fusion framework for measurable 3D characterization of mining-induced surface cracks under real topographic conditions. High-resolution UAV orthomosaics were used to extract crack semantics with a dual residual-attention U-Net (DRA-UNet). The resulting crack masks were skeletonized and vectorized, and a k-dimensional tree (KDTree)-based spatial matching strategy was applied to link crack centerlines with TLS point clouds, thereby constructing semantic crack point clouds. The framework was evaluated at the Huojitu Mine, Shenmu City, China, using approximately 5000 crack samples. DRA-UNet achieved precision, recall, F1-score, and mean intersection over union values of 85.13%, 77.84%, 81.32%, and 70.26%, respectively. Compared with conventional 2D length measurements, the proposed 3D estimation reduced the mean relative error from 4.49% to 2.64%. Width validation at 24 field points yielded a mean relative error of 4.47%. These results show that UAV–TLS fusion can bridge the gap between planar crack detection and true 3D geometric characterization, providing a practical tool for mining-induced ground damage monitoring. Full article

The documentation and management of heritage structures in arid environments present significant challenges due to environmental deterioration, limited historical records, and the complexity of capturing irregular architectural forms. This study presents a structured Scan-to-HBIM workflow integrating terrestrial laser scanning (TLS), photogrammetry, and Heritage Building Information Modelling (HBIM) for the digital documentation of Qasr Harrana, one of the most significant examples of Umayyad desert architecture in Jordan. The proposed workflow combines reality-capture technologies with parametric modelling to generate an information-rich HBIM model that supports the systematic organization of geometric, architectural, and condition-related data. The methodology includes field data acquisition using TLS and digital photography, point cloud processing and registration, photogrammetric image integration, geometric reconstruction, and the development of parametric HBIM components representing key architectural elements of the monument. The resulting model was assessed through geometric verification procedures and was used to document architectural features, spatial organization, and visible deterioration conditions. The study demonstrates how established reality-capture technologies can be integrated within a coherent documentation framework to support the creation of accurate and reusable digital heritage records. Rather than proposing new acquisition algorithms or modelling techniques, the contribution of this research lies in the structured adaptation and implementation of existing Scan-to-HBIM methods for the documentation of desert heritage architecture. The resulting HBIM model provides a comprehensive digital archive that can facilitate future conservation, management, and research activities while improving the accessibility and long-term usability of heritage documentation data. Full article

In arid and hyper-arid regions, the temporal dynamics of surface soil moisture (SSM) significantly affect surface erodibility, as well as aeolian sediment transport and deposition processes. Conventional measurement techniques fail to meet the requirements of fine spatiotemporal resolution monitoring, and terrestrial laser scanning (TLS) possesses unique advantages and great application potential for SSM detection. In this study, the accuracy and feasibility of TLS for retrieving SSM in sandy desert were evaluated. The experimental results reveal that sand particle size, measurement distance and SSM are three important factors affecting laser signal amplitude. The relationship between measurement distance and amplitude follows a three-stage variation pattern that can be well fitted by an exponential decay model. Amplitude fluctuates with sand particle size, and the fluctuations become more pronounced at longer measurement distances or higher SSM levels. A strong linear correlation exists between amplitude and SSM within the range of 2.5–20%. Accordingly, particle size-dependent calibration equations were established to estimate SSM from TLS observations. Field validation was further conducted to clarify the applicable scope and constraints of this method. This approach provides an innovative solution for the high-precision, rapid, non-contact and dynamic monitoring of SSM in arid sandy desert. Full article

Accurate characterization of coastal environments requires high-resolution spatial data and robust analytical workflows which are capable of capturing the complexity of intertidal surfaces and engineered shoreline structures. This study presents a field application of terrestrial and vessel-based LiDAR with AI-assisted point-cloud processing to support coastal mapping and shoreline analysis in a complex tidal flat environment. Field measurements were conducted in a geomorphologically dynamic estuarine system dominated by wide tidal flats and diverse natural and artificial coastal features. A comparative assessment of terrestrial laser scanning (TLS) and vessel-based mobile mapping system (MMS) data revealed distinct platform characteristics: TLS provided long-range, high-fidelity geometric measurements suitable for broad intertidal zones, whereas vessel-based MMS offered rapid and continuous coverage but exhibited range-related limitations in offshore and distal areas. To extract key coastal features, an AI-assisted workflow was implemented in Trimble Business Center (TBC), primarily using the TLS dataset as the input for feature extraction. The vessel-based MMS data were used to evaluate complementary acquisition characteristics, including coverage continuity, scanning range, accessibility, and visualization of coastal features, rather than as direct input for joint TLS–MMS AI classification. A manually curated training dataset consisting of 80 object-level point-clouds—cars, streetlights, powerlines, and fences—was used to train a custom extraction model, while TBC’s built-in AI tools were employed to automatically separate ground surfaces, vegetation, and built structures from the TLS point-cloud. The TLS-based AI-assisted workflow produced a multilayered representation of the TLS data, enabling detailed delineation of intertidal flats, engineered shoreline structures, and adjacent artificial objects. Quantitative evaluation based on comparison with manually annotated ground truth yielded an overall F1-score of approximately 0.87, indicating practical extraction performance for the evaluated object types. The results highlight the complementary strengths of TLS and vessel-based MMS data and the practical applicability of TLS-based AI-assisted point-cloud processing in complex coastal settings. The results indicate that TLS-based AI-assisted point-cloud processing, supported by vessel-based MMS comparison, can provide an operationally useful approach for coastal feature extraction, shoreline monitoring, and coastal infrastructure assessment in complex tidal flat environments. By improving the acquisition, interpretation, and management of high-resolution coastal spatial information, the proposed workflow can support sustainable shoreline monitoring, coastal infrastructure maintenance, and evidence-based coastal environmental management in vulnerable tidal flat environments. Full article

Efficient bridge scanning and documentation are crucial for creating reliable digital 3D models. However, scanning workflows often rely on implicit practitioner experience rather than standardized protocols. This paper presents practical insights derived from a Multiple Case Study (MCS) of ten heterogeneous, real-world bridges in Germany. The study evaluates Terrestrial Laser Scanning (TLS), Mobile Laser Scanning (MLS) and Unmanned Aerial Systems (UAS) photogrammetry. The findings isolate distinct performance trade-offs. TLS offers high accuracy but suffers from shadowing occlusions. Conversely, UAS provides operational flexibility but introduces geometric vulnerabilities, including photogrammetric reconstruction noise on fine structures and SLAM trajectory drift on vibrating spans. To unify these insights, a generalized, BPMN-compliant process model mapping the complete data acquisition lifecycle under legal and spatial constraints is defined. This research provides an actionable, practical guide to optimize data quality and efficiency in structural engineering workflows. Full article