Search Results (3,436)

Digital Elevation Models (DEMs) provide essential information for decision-making in precision agriculture. This study evaluated the altimetric quality of DEMs generated by Remotely Piloted Aircraft (RPA) platforms, the influence of flight direction, and the effect of mechanically disturbed soil surface conditions. We obtained data from a 900 m² area. Flights were conducted under pre- and post-mechanization conditions using a reversible plow, with flights in both longitudinal and transverse directions. We processed images using Structure-from-Motion (SfM) techniques to generate dense point clouds and DEMs. Statistical analyses relied on raster statistics and elevation cross-section transects of microtopography, were evaluated via descriptive statistics, ANOVA, Tukey’s HSD tests, and spatialization with micro-variation classification. Significant differences emerged among the evaluated models (p < 0.001), with Phantom-derived DEMs showing systematically higher elevations than Mavic models (617.31 ± 0.16 m vs. 605.41 ± 0.23 m, respectively). Post-plowing longitudinal flights showed the least variation, indicating greater altimetric consistency after secondary soil preparation. Conversely, the pre-plowing transverse flight (Mavic Flight 2) produced the largest errors. Quantitative assessment of topographic profiles revealed high morphological correspondence between platforms, with Pearson correlation coefficients ranging from 0.84 to 0.96 after vertical normalization, confirming that terrain morphology was preserved despite systematic vertical offsets. The effect of flight direction was more pronounced before soil preparation; after harrowing (a homogeneous surface), the difference between directions decreased, but longitudinal flights maintained an advantage, while transverse flights (especially Mavic) tended to overestimate elevations spatially. Full article

Point-cloud analysis of sedimentary outcrops using Unmanned Aerial Vehicle (UAV) oblique photogrammetry is a crucial approach to sedimentary system characterization, stratigraphic correlation, and petroleum exploration analog studies. In large-scale field settings, however, outcrops are often scattered and fragmented, vegetation and soil cover is extensive, and class imbalance is pronounced. Manual interpretation is labor-intensive, while existing clustering algorithms, conventional machine learning methods, and general-purpose point-cloud segmentation networks struggle to simultaneously ensure geometric fidelity, rare-class recognition, and multi-scale feature integration. To address these challenges, we propose a method for extracting sedimentary outcrop point clouds from field surface point clouds using a UAV oblique photogrammetry acquisition strategy. The core segmentation module of the method, sedimentary cross-scale self-attention network (SedCSA-Net), is an enhanced version of PointNet++ that integrates collaborative improvements across four dimensions: data augmentation, sampling strategy, feature encoding, and loss optimization. Taking the Cretaceous Qingshuihe Formation in the Louzhuangzi area of the southern Junggar Basin as a case study, our experimental results indicate that SedCSA-Net overcomes the natural variability of UAV oblique photogrammetry point clouds—such as shadows, voids, and uneven density—achieving a mean Intersection over Union(mIoU) of 89.51% and an Overall Accuracy(OA) of 96.08%, with an outcrop-class Intersection over Union(IoU) of 86.90%. Attitude measurements derived from segmentation results deviate by less than 3° from manually annotated references, demonstrating that the proposed framework provides an end-to-end, generalizable approach for intelligent segmentation, geometric reconstruction, and attitude extraction of large-scale sedimentary outcrop point clouds. Full article

Industrial robotic arms are fundamental components of modern automated production lines, executing critical tasks such as welding, painting, and assembly. Such high-precision operations often require careful manual tool positioning during the initial setup. To automate and refine this process, a highly accurate kinematic model of the robot is essential. In this paper, the authors propose a novel algorithm for kinematic parameter calibration using photogrammetry to track multiple robot links simultaneously. The proposed multi-link calibration approach provides a more precise parameter estimation and introduces the practical possibility of continuous parameter refinement while the robot executes its primary operational tasks. The superior accuracy and robustness of the proposed methodology are confirmed through comprehensive simulation experiments, and the feasibility of the approach is successfully demonstrated on a real robotic arm. Full article

The documentation and reconstruction of fragile underwater organic artifacts remain among the most challenging tasks in digital heritage practice. This study presents a conservation-first, contact-minimizing protocol applied to a rare Byzantine leather bag recovered from the commercial port of Rhodes, Greece. Due to its incomplete preservation and structural instability, exclusively non-invasive methodologies were employed. High-resolution close-range photogrammetry and structured-light 3D scanning were integrated to capture both micro-topographic detail and metrically stable geometry. Quantitative deviation analysis (nearest-neighbor cloud-to-mesh distances) indicated that most geometric differences remain below 0.5 mm. The resulting models were processed through controlled mesh optimization, UV remapping, and conservation-oriented digital completion workflows. In addition, radiance field visualization techniques such as Gaussian Splatting were explored as complementary visualization approaches for incomplete geometries. These methods were evaluated primarily in terms of visual continuity and interpretative support rather than as reconstruction tools. The study demonstrates that the integration of photogrammetry, structured-light scanning, and Gaussian Splatting can significantly enhance the documentation and visualization of fragile underwater organic heritage. At the same time, it highlights the necessity of methodological transparency and ethical framing when incorporating probabilistic reconstructions into conservation workflows. Full article

While infrastructure asset management increasingly relies on high-resolution drone imagery, existing workflows suffer from fragmented information management and dependence on costly local processing infrastructure. This paper addresses these limitations by using a cloud-native spatial intelligence hub that converts raw inspection imagery into an interactive and queryable three-dimensional information layer. The system integrates a timeout-resilient orchestration layer for photogrammetry pipelines, a multi-user three-dimensional environment for collaborative review, and a PostGIS-backed spatial database that stores defects as georeferenced anchors. We further introduce a spatial anchoring workflow mapping three-dimensional interactions to world coordinates, retrieving context-relevant images via frustum-based visibility scoring. Evaluated on real inspection datasets, the serverless architecture achieved end-to-end reconstruction in under one hour with sub-25 ms query latency. Results indicate that acquisition geometry, particularly oblique convergent viewpoints, is a stronger predictor of reconstruction complexity than image count. This work establishes a reproducible reference architecture, enabling a transition from file-centric documentation to traceable, spatially indexed evidence management for infrastructure Digital Twins. Full article

Stone arch bridges represent culturally significant heritage assets that exhibit distinct regional characteristics. At present digital preservation largely attends to geometric modeling and typically neglects the identification and conformance of core culture genes. This oversight has resulted in a disconnect between technological application and core heritage values, a prevalent issue globally. To address this, this study employs cultural gene theory to formulate a systematic framework for investigating the architectural cultural genes of stone arch bridges from the three dimensions: material–morphological, technical–behavioral, and cultural–symbolic. This study takes the Hongji Bridge in Handan as an example and uses literature research and 3D laser scanning and UAV oblique photogrammetry and qualitative extraction and visual presentation of the architectural genetic characteristics of stone arch bridges. This study identifies 11 core genetic indicators from the dimensions of genetic architecture, inheritance, and evolution, for the architectural cultural genes for the Chinese stone arch bridges The Zhaozhou Bridge (China) and Serranos Bridge (Europe)’s cross-cultural comparative analyses are adopted to validate the generalizability of the framework and the genetic uniqueness of the Chinese stone arch bridge. This research introduces a gene-based model of digital conservancy that fosters the transition of heritage preservation from technology-driven to value-driven. Full article

This study presents the diagnostic and conservation work carried out on the Roman mosaic of the South cubiculum in the Latomia dei Niccolini (Marsala, western Sicily). The mosaic, decorated with polychrome tesserae featuring a kantharos motif, presented severe structural damage, including fractures, subsurface voids, and progressive material loss. To assess the causes of deterioration and design an effective conservation strategy, an integrated approach combining non-invasive geophysical and 3D survey methods was applied. Ground-penetrating radar (GPR) was selected as the main diagnostic tool because it allows high-resolution subsurface imaging while preserving the integrity of the fragile mosaic surface. By utilizing high-frequency 2 GHz antennas and complementary video inspection, a significant subsurface cavity beneath the mosaic preparation layer was successfully mapped, determining its critical relationship with the main diagonal surface fracture. Simultaneously, laser scanning and close-range photogrammetry enabled the creation of accurate 3D models supporting both documentation and restoration planning. The conservation concluded with surface cleaning, mortar consolidation, and the successful structural detachment and relocation of the compromised section onto a lightweight support for future museum display. The findings demonstrate that integrating 3D digital and geophysical data provides a quantitative, low-risk roadmap for preserving highly vulnerable archaeological floorings, moving beyond qualitative technical documentation to establish a replicable preservation framework. Full article

The increasing availability of airborne LiDAR data supports advanced three-dimensional analysis of urban vegetation. However, the development of deep learning methods for tree species classification remains limited by the lack of annotated datasets at the individual-tree level. This study presents UrbanTree3D, a field-validated dataset comprising segmented individual trees extracted from airborne LiDAR point clouds and enriched with species information from field inventory data. The dataset was generated through a structured workflow, including noise removal, vegetation extraction, height normalization based on a digital elevation model (DEM), and temporal consistency verification. Individual trees were segmented using a hybrid approach integrating DBSCAN and Watershed algorithms, and subsequently matched to field inventory data using a nearest neighbor method. A field validation campaign was conducted to ensure data reliability. The final dataset contains 152 individual urban trees and includes six tree species. It provides high-quality annotations, consistent point clouds, and field validation data, supporting its use for training and evaluating deep learning models. UrbanTree3D addresses the current shortage of annotated LiDAR datasets and supports applications in urban forestry, smart cities and urban digital twins. Full article

Background/Objective: Accurate digital recording of implant position is essential for achieving passive fit and predictable outcomes in complete-arch implant-supported prostheses. However, complete-arch cases remain challenging because of increased inter-implant distances, limited anatomical landmarks, soft tissue mobility, scan body-related variables, and cumulative errors during data acquisition and file registration. This narrative review aims to evaluate current intraoral and extraoral digital implant position recording techniques from a clinical decision-making perspective. Methods: A structured narrative literature search was conducted in PubMed from database inception to 15 May 2026 and was supplemented by manual screening of reference lists of key systematic reviews and eligible articles. Systematic reviews, meta-analyses, clinical studies, comparative in vitro studies, dental technique articles, and clinical reports relevant to complete-arch digital implant position recording were considered. Higher-level and clinically relevant evidence was prioritized, whereas technique reports were included primarily for emerging workflows with limited clinical evidence. Results: Intraoral techniques include non-splinted and splinted scan body protocols, calibrated implant scan bodies, calibrated frameworks, and auxiliary reference strategies. These methods may be clinically efficient but remain sensitive to scan path, scanner technology, landmark availability, scan body design, implant distribution, and operator-related factors. Extraoral techniques include stereophotogrammetry, camera- or smartphone-assisted photogrammetric systems, reverse impression workflows, and laboratory scanner-based digitization. These approaches may reduce intraoral stitching errors in complex edentulous arches, but usually require complementary datasets for soft tissue morphology, prosthetic contours, antagonist dentition, and maxillomandibular relationships. Conclusions: Direct intraoral scanner (IOS) protocols may be appropriate in favorable complete-arch situations with accessible scan bodies, limited inter-implant distances, and stable reference geometry. In clinically demanding cases requiring greater cross-arch accuracy, stereophotogrammetry, intraoral photogrammetry, or calibrated scanning approaches may provide more controlled implant position recording. Reverse impression and model-based workflows are particularly useful when a verified interim prosthesis, verification jig, or cast-based reference is available. Regardless of the selected technique, accurate integration of implant coordinates with soft tissue, prosthetic contour, antagonist arch, and occlusal data remains essential. Full article

Rurbanization and peri-urbanization are among the most dynamic territorial processes affecting metropolitan regions in Morocco, particularly within the Casablanca–Settat region. These transformations, driven by rapid urban growth, demographic pressure, and socio-economic change, generate complex transitional spaces between rural and urban environments. In this context, the present study proposes a hybrid methodology for detecting, classifying, and analyzing the rural–urban continuum by using remote sensing data and artificial intelligence techniques. The approach integrates Sentinel-2 satellite imagery, spectral indices, Global Human Settlement Layer datasets, and socio-demographic indicators derived from the Moroccan census. Two models, Self-Organizing Maps (SOM) and Graph Neural Networks (GNN), were applied to classify territories into four categories: urban, peri-urban, rurban, and rural. Model outputs were combined with expert-based decision rules to improve classification robustness and interpretability. The SOM model achieved up to $89.3\%$ agreement with expert classifications and a Cohen’s Kappa coefficient of $0.842$, demonstrating strong interpretability and consistency, while the GNN model reached $53\%$ agreement and effectively modeled spatial dependencies and neighborhood interactions. Diachronic analysis between 2014 and 2024 revealed a $54\%$ increase in peri-urban municipalities, a $24\%$ decrease in rurban territories, and a decline in rural municipalities, highlighting intensified urban sprawl and fragmentation of agricultural landscapes. Beyond its scientific contribution, this study provides a valuable decision-support framework for urban planners, environmental agencies, and policy makers involved in territorial governance and sustainable development. It can support land-use planning, monitoring of urban sprawl, protection of agricultural lands, and the implementation of adaptive territorial policies aimed at improving the resilience and sustainability of rurban environments. Full article

The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies. Full article

Land surface temperature (LST) is crucial for understanding winter landslide evolution. This study combines Unmanned Aerial Vehicle (UAV) photogrammetry and infrared thermography (IRT) to monitor winter landslides in China’s Wumeng Mountain region. Using the Yangjiazhai landslide—induced by underground coal mining—as a case study, we demonstrate significant correlations between IRT-detected LST anomalies and surface cracks: (1) cracks with elevated temperatures are likely connected to subsurface goaf zones; (2) excessively widened cracks show no thermal anomalies due to enhanced air convection. The research reveals that key landslide components have distinct LST signatures, governed by differential soil–rock moisture and crack networks. For accurate high-altitude winter LST acquisition, UAV thermal surveys should be conducted under overcast, fog-free conditions to reduce solar interference. This validates UAV visible–infrared fusion for extracting landslide boundaries, cracks, slumping zones, bedrock patterns, and moisture distribution. The methodology establishes a new pathway for investigating winter landslide deformation and instability, confirming IRT’s operational viability in high-altitude alpine regions. Full article

The development of digital photogrammetry techniques has revolutionized the study of marine ecosystems, enabling the generation of high-precision three-dimensional models from conventional imagery. Structure from Motion (SfM) algorithms have become effective tools for mapping and monitoring underwater habitats, offering a non-invasive and cost-effective alternative to traditional methods. This study presents a pilot methodological validation of SfM-based underwater photogrammetry for the non-invasive morphometric monitoring of vulnerable benthic species, using Pinna rudis. The research focused on refining photogrammetric methodologies for marine conservation, addressing technical challenges such as variations in light conditions, water turbidity, and image acquisition complexity. The study area, the Cabrera Archipelago Maritime-Terrestrial National Park, is a pristine marine environment in the western Mediterranean, hosting diverse benthic communities, including an abundant Pinna rudis population. Data acquisition comprises sampling by scuba diving techniques at depths ranging from 26 to 31 m, performed during the July 2022 field campaign within a permanent demographic plot established in 2013 and the methodology applied involved generating three-dimensional models using SfM, allowing for direct measurements of the seabed and extraction of morphometric parameters of sessile species. The characterization of the Pinna rudis aggregation was based on specimen density and size structure, determined using maximum shell width. The 3D model of the pilot plot covers 86.1 m², hosting 31 individuals. Morphometric measurements derived from SfM-based 3D models were validated against in situ diver measurements of maximum shell width. The results showed that the average maximum width obtained from 3D models (15.19 ± 3.23 cm) was consistent with in situ measurements (15.35 ± 3.48 cm). The mean difference between methods was −0.16 ± 0.82 cm, indicating a negligible systematic bias. The mean absolute error was 0.65 cm, corresponding to an average relative error of 4.34%, and a strong linear relationship was observed between both methods (r = 0.97). These results confirm that underwater photogrammetry is a reliable and non-invasive tool for monitoring vulnerable benthic species, providing high-resolution spatial and morphometric data to support conservation strategies in marine protected areas and allowing the collection of additional data compared to in situ surveys. Full article

Ground-surface temperature (GST) in maritime Antarctic ice-free areas is influenced by atmospheric forcing, snow cover, surface energy and topography. Previous PERMATHERMAL studies in Livingston and Deception Islands have shown changes in air and ground-surface thermal regimes, with fewer cold conditions, greater thawing influence and strong snow-cover modulation. However, the interval in which GST responds effectively to radiative and topographic forcing remains poorly explored. We characterize the station- and season-specific timing of the thermally effective GST thawing period and evaluate topographic and modeled potential controls on its thermal intensity and cumulative effect around the Spanish Antarctic Station Juan Carlos I, Hurd Peninsula, Livingston Island. Onset and end were objectively delimited by using three consecutive days with daily mean GST > 0.5 °C and daily thermal amplitude > 1.0 °C. Hourly GST records from six PERMATHERMAL stations were combined with potential radiation, potential insolation and topographic variables derived from a high-resolution UAV-based DEM. Accumulated thawing degree days were strongly influenced by period duration. Mean thermal intensity was primarily associated with elevation, while mean modeled potential radiation provided additional explanatory power only when combined with elevation. This UAV–GIS–GST approach provides a simple framework for assessing local surface–atmosphere coupling in remote Antarctic ice-free areas. Full article