Search Results (334)

Blasting operations in surface mining pose a risk of flyrock, which is a critical safety concern for both personnel and infrastructure. This study presents the use of unmanned aerial vehicles (UAVs) and photogrammetric techniques to improve the accuracy of blast design, particularly in relation to controlling burden values and reducing flyrock. The research was conducted in a basalt quarry in Lower Silesia, where high rock fracturing complicated conventional blast planning. A DJI Mavic 3 Enterprise UAV was used to capture high-resolution aerial imagery, and 3D models were created using Strayos software. These models enabled precise analysis of bench face geometry and burden distribution with centimeter-level accuracy. The results showed a significant improvement in identifying zones with improper burden values and allowed for real-time corrections in blasthole design. Despite a ten-fold reduction in the number of images used, no loss in model quality was observed. UAV-based surveys followed software-recommended flight paths, and the application of this methodology reduced the flyrock range by an average of 42% near sensitive areas. This approach demonstrates the operational benefits and enhanced safety potential of integrating UAV-based photogrammetry into blasting design workflows. Full article

UAV platforms equipped with RTK positioning and LiDAR sensors are increasingly used for landslide monitoring, offering frequent, high-resolution surveys with broad spatial coverage. In this study, we applied high-frequency UAV-based monitoring to the active Baldiola earthflow (Northern Apennines, Italy), integrating 10 UAV–LiDAR and photogrammetric surveys, acquired at average intervals of 14 days over a four-month period. UAV-derived orthophotos and DEMs supported displacement analysis through homologous point tracking (HPT), with robotic total station measurements serving as ground-truth data for validation. DEMs were also used for multi-temporal DEM of Difference (DoD) analysis to assess elevation changes and identify depletion and accumulation patterns. Displacement trends derived from HPT showed strong agreement with RTS data in both horizontal (R² = 0.98) and vertical (R² = 0.94) components, with cumulative displacements ranging from 2 m to over 40 m between April and August 2024. DoD analysis further supported the interpretation of slope processes, revealing sector-specific reactivations and material redistribution. UAV-based monitoring provided accurate displacement measurements, operational flexibility, and spatially complete datasets, supporting its use as a reliable and scalable tool for landslide analysis. The results support its potential as a stand-alone solution for both monitoring and emergency response applications. Full article

This research aims to investigate the Italian architectural heritage in Brazil through the analysis of the ‘Santa Casa de Misericórdia’ hospital in São Carlos, in the state of São Paulo. As part of the KNOW.IT national project, the work aims to recover and digitally enhance Italian heritage abroad from the 19th and 20th centuries. The buildings analysed were either designed or built by Italian architects who emigrated to South America or constructed using materials and techniques typical of Italian architecture of those years. The hospital, designed by the Italian architect Samuele Malfatti in 1891, was chosen for its historical value and its role in the urban context of the city of São Carlos, which, moreover, continues to perform its function even today. The study aims to create a digital archive with 3D models and two-dimensional graphical drawings. The methodology includes historical analysis, photogrammetric survey, and digital modelling using Agisoft Metashape and 3DF Zephyr software. A total of 636 images were processed, with the maximum resolution achieved in the models being 3526 × 2097 pixels. The results highlight the influence of Italian architecture on late 19th-century São Carlos and promote its virtual accessibility and wide-ranging knowledge. Full article

Innovative applications of high-resolution airborne imaging are explored for detecting grapevine diseases. Driven by the motivation to enhance early disease detection, the method’s effectiveness lies in its capacity to identify isolated cases of grapevine yellows (Flavescence dorée and Bois Noir) and trunk disease (Esca complex), crucial for preventing the disease from spreading to unaffected areas. Conducted over a 17 ha vineyard in the Forlì municipality in Emilia-Romagna (Italy), the aerial survey utilized a photogrammetric camera capturing centimeter-level resolution images of the whole area in 17 minutes. These images were then processed through an automated analysis leveraging RGB-based spectral indices (Green–Red Vegetation Index—GRVI, Green–Blue Vegetation Index—GBVI, and Blue–Red Vegetation Index—BRVI). The analysis scanned the 1.24 · 10⁹ pixels of the orthomosaic, detecting 0.4% of the vineyard area showing evidence of disease. The instances, density, and incidence maps provide insights into symptoms’ spatial distribution and facilitate precise interventions. High specificity (0.96) and good sensitivity (0.56) emerged from the ground field observation campaign. Statistical analysis revealed a significant edge effect in symptom distribution, with higher disease occurrence near vineyard borders. This pattern, confirmed by spatial autocorrelation and non-parametric tests, likely reflects increased vector activity and environmental stress at the vineyard margins. The presented pilot study not only provides a reliable detection tool for grapevine diseases but also lays the groundwork for an early warning system that, if extended to larger areas, could offer a valuable system to guide on-the-ground monitoring and facilitate strategic decision-making by the authorities. Full article

Photogrammetry can be a valuable tool for understanding landscape evolution and natural hazards such as landslides. However, factors such as vegetation cover, shadows, and unstable ground can limit its effectiveness. Using photos across time to monitor an area with unstable or changing ground conditions results in fewer tie points between images across time, and often leads to low comparative accuracy if single-epoch (i.e., classical) photogrammetric processing approaches are used. This paper presents a study evaluating the co-alignment approach applied to fixed terrestrial timelapse photos at an active landslide site. The study explores the comparative accuracy of reconstructed surface models and the location and behavior of tie points over time in relation to increasing levels of global change due to landslide activity and rockfall. Building upon previous work, this study demonstrates that high comparative accuracy can be achieved with a relatively low number of inter-epoch tie points, highlighting the importance of their distribution across stable ground, rather than the total quantity. High comparative accuracy was achieved with as few as 0.03 percent of the overall co-alignment tie points being inter-epoch tie points. These results show that co-alignment is an effective approach for conducting change detection, even with large degrees of global changes between surveys. This study is specific to the context of geoscience applications like landslide monitoring, but its findings should be relevant for any application where significant changes occur between surveys. Full article

The increasing pace of climate-driven changes in forest ecosystems calls for reliable remote sensing techniques for quantifying above-ground carbon storage. In this article, we compare the methodology and results of traditional field surveys, mobile laser scanning, optical drone imaging and photogrammetry, and both drone-based and light aircraft-based aerial laser scanning to determine forest stand parameters, which are suitable to estimate carbon stock. Measurements were conducted at four designated sampling points established during a large-scale project in deciduous and coniferous tree stands of the Dudles Forest, Hungary. The results of the surveys were first compared spatially and quantitatively, followed by a summary of the advantages and disadvantages of each method. The mobile laser scanner proved to be the most accurate, while optical surveying—enhanced with a new diameter measurement methodology based on detecting stem positions from the photogrammetric point cloud and measuring the diameter directly on the orthorectified images—also delivered promising results. Aerial laser scanning was the least accurate but provided coverage over large areas. Based on the results, we recommend adapting our carbon stock estimation methodology primarily to mobile laser scanning surveys combined with aerial laser scanned data. Full article

In the field of building archaeology, the analysis of wall surfaces represents a fundamental tool for the study of an architecture and its construction phases. In fact, masonry stores valuable information regarding not only used materials and construction techniques but also transformations happen over time for natural events or anthropic interventions. The traditional approach to the analysis of building materials is mainly based on direct observation and manual annotations based on orthophotos obtained through photogrammetric surveys. This process, while providing a high degree of accuracy and understanding, is extremely time- and resource-consuming. In addition, the lack of standardised procedures for the statistical analysis of measurements leads to data that are difficult to compare for different contexts. Time and subjectivity are ultimately the two main limitations that most hinder the diffusion of the mensiochronological approach and for this reason, the most recent artificial intelligence solutions for the segmentation and extraction of measurements of individual masonry components will be addressed. Finally, a workflow will be presented based on image segmentation using machine learning models and the automatic extraction and statistical analysis of measurements using a script designed specifically by the author for the mensiochronological analysis of Roman brick masonry. Full article

The automation in image acquisition and processing using UAV drones has the potential to acquire terrain data that can be utilized for the accurate production of 2D and 3D digital data. In this study, the DJI Phantom 4 drone was employed for large-scale topographical mapping, and based on the photogrammetric Structure-from-Motion (SfM) algorithm, drone-derived point clouds were used to generate the terrain DSM, DEM, contours, and the orthomosaic from which the topographical map features were digitized. An evaluation of the horizontal (X, Y) and vertical (Z) coordinates of the UAV drone points and the RTK-GNSS survey data showed that the Z-coordinates had the highest MAE(_X,Y,Z), RMSE_(X,Y,Z) and Accuracy_(X,Y,Z) errors. An integrated georeferencing of the UAV drone imagery using the mobile RTK-GNSS base station improved the 2D and 3D positional accuracies with an average 2D (X, Y) accuracy of <2 mm and height accuracy of −2.324 mm, with an overall 3D accuracy of −4.022 mm. Geometrically, the average difference in the perimeter and areas of the features from the RTK-GNSS and UAV drone topographical maps were −0.26% and −0.23%, respectively. The results achieved the recommended positional accuracy standards for the production of digital geospatial data, demonstrating the cost-effectiveness of low-cost UAV drones for large-scale topographical mapping. Full article

This paper presents the documentation, study activities, and possible applications of 3D digital models for the analysis and reconstruction of some examples of spheroidal stone projectiles—launched during the Sullan siege in 89 BC—now preserved in the Archaeological Park of Pompeii. The research proposes a methodology to derive best-fitting shapes that most closely adhere to the partially reconstructed image-based geometries. This allows a comparison with the circular ballistic impact traces still present on the ashlars of the northern city walls, as discovered by archaeologists about a hundred years ago. The results facilitate more precise ballistic calculations for the reconstruction of the elastic torsion weapons and their launching power. Full article

This volume brings together the reflections of those who have been committed to building a dialogue around the results achieved (and currently underway) by the research project “Comparative Analysis and Certified Reconstructions for a correct experimental archeology: Roman Scorpions and Ballistae for the Imperial mechanical culture, origin of European identity [...] Full article

Unmanned aerial vehicles (UAVs) have increasingly proven to be flexible tools for mapping mine terrain, offering expedient and precise data compared to alternatives. Photogrammetric outputs are particularly beneficial in open pit operations and waste dump areas, since they enable cost-effective and reproducible digital terrain models. Meanwhile, UAV-based LiDAR has proven invaluable in situations where uniform ground surfaces, dense vegetation, or steep slopes challenge purely photogrammetric solutions. Recent advances in machine learning and deep learning have further enhanced the capacity to distinguish critical features, such as vegetation and fractured rock surfaces, thereby reducing the likelihood of accidents and ecological damage. Nevertheless, scientific gaps remain to be researched. Standardization around flight practices, sensor selection, and data verification persists as elusive, and most mining sites still rely on limited, multi-temporal surveys that may not capture sudden changes in slope conditions. Complexity lies in devising strategies for rehabilitated dumps, where post-mining restoration efforts involve vegetation regrowth, erosion mitigation, and altered land use. Through expanded sensor integration and refined automated analysis, approaches could shift from information gathering to ongoing hazard assessment and environmental surveillance. This evolution would improve both safety and environmental stewardship, reflecting the emerging role of UAVs in advancing a more sustainable future for mining. Full article

With the rapid advancement of smart forestry, 3D reconstruction and the extraction of structural parameters have emerged as indispensable tools in modern forest monitoring. Although traditional methods involving LiDAR and manual surveys remain effective, they often entail considerable operational complexity and fluctuating costs. To provide a cost-effective and scalable alternative, this study introduces FS-MVSNet—a multi-view image-based 3D reconstruction framework incorporating feature pyramid structures and attention mechanisms. Field experiments were performed in three representative forest parks in Beijing, characterized by open canopies and minimal understory, creating the optimal conditions for photogrammetric reconstruction. The proposed workflow encompasses near-ground image acquisition, image preprocessing, 3D reconstruction, and parameter estimation. FS-MVSNet resulted in an average increase in point cloud density of 149.8% and 22.6% over baseline methods, and facilitated robust diameter at breast height (DBH) estimation through an iterative circle-fitting strategy. Across four sample plots, the DBH estimation accuracy surpassed 91%, with mean improvements of 3.14% in AE, 1.005 cm in RMSE, and 3.64% in rRMSE. Further evaluations on the DTU dataset validated the reconstruction quality, yielding scores of 0.317 mm for accuracy, 0.392 mm for completeness, and 0.372 mm for overall performance. The proposed method demonstrates strong potential for low-cost and scalable forest surveying applications. Future research will investigate its applicability in more structurally complex and heterogeneous forest environments, and benchmark its performance against state-of-the-art LiDAR-based workflows. Full article

This article presents the results of high-resolution multimodal remote sensing surveys which were performed in the Roman city of Caesarea Maritima at the sunken Herodian harbour of Sebastos. A joint team of scholars from the Universities of Malta and Haifa conducted the surveys at the area of the harbour’s entrance in order to answer questions related to its original architecture, long-term functioning, and gradual degradation processes. The core methodology employed comprised teams of divers performing a meticulous photogrammetric survey in order to generate a high-resolution 3D plan of the harbour’s entrance. The results present two different architectural styles on either side of the harbour entrance, which suggests two different building stages, potentially deriving from a late renovation attempt. The current state of the entrance channel, still deep and wide enough for the passage of vessels despite collapse and sedimentation processes, suggests the long-term functionality of the entrance, even while other parts of the harbour have structurally deteriorated and gone out of use. Full article

The generation of digital aerial photogrammetry products using unmanned aerial vehicle-digital aerial photogrammetry (UAV-DAP) has become an essential task due to the increasing use of UAVs in the world of geomatics, thanks to their low cost and spatial resolution. Therefore, it is relevant to explore the performance of new digital cameras equipped in UAVs using electronic rolling shutters instead of ideal mechanical or global shutter cameras to achieve accurate and reliable photogrammetric products, if possible, while minimizing workload, especially for their application in projects that require a high level of detail. In this paper, we analyse performance using oblique images along the perimeter (3D perimeter) on a flat area, i.e., with slopes of less than 3%. The area was photogrammetrically surveyed with a DJI (Dà-Jiāng Innovations) Inspire 2 multirotor UAV equipped with a Zenmuse X5S rolling shutter camera. The photogrammetric survey was accompanied by a Global Navigation Satellite System (GNSS) survey, in which dual frequency receivers were used to determine the ground control points (GCPs) and checkpoints (CPs). The study analysed different scenarios, including the combination of forward and transversal strips and oblique images. After examining the ideal scenario with the least root mean square error (RMSE), six different combinations were analysed to find the best location for the GCPs. The most significant results indicate that the optimal calibration of the camera is obtained in scenarios including oblique images, which outperform the rest of the scenarios for achieving the lowest RMSE (2.5x the GSD in Z and 3.0x the GSD in XYZ) with optimum GCPs layout; with non-ideal GCPs layout, unacceptable errors can be achieved (11.4x the GSD in XYZ), even with ideal block geometry. The UAV-DAP rolling shutter effect can only be minimised in the scenario that uses oblique images and GCPs at the edges of the overlapping zones and the perimeter. Full article

We applied UAV (Unmanned Aerial Vehicle) and ALS (Airborne Laser Scanning) remote sensing methods to identify terrain obstacles encountered during timber extraction in the skidding process with the aim of proposing accessibility solutions to the inner parts of forest stands using skidding trails. At the Vítovický žleb site, located east of Brno in the South Moravian Region of the Czech Republic, we analysed the accuracy of digital terrain models (DTMs) created from UAV LiDAR (Light Detection and Ranging), RGB (Red–Green–Blue) UAV, ALS data taken on site and publicly available LiDAR data DMR 5G (Digital Model of Relief of the Czech Republic, 5th Generation, based on airborne laser scanning, providing pre-classified ground points with an average density of 1 point/m²). UAV data were obtained using two types of drones: a DJI Mavic 2 mounted with an RGB photogrammetric camera and a GeoSLAM Horizon laser scanner on a DJI M600 Pro hexacopter. We achieved the best accuracy with UAV technologies, with an average deviation of 0.06 m, compared to 0.20 m and 0.71 m for ALS and DMR 5G, respectively. The RMSE (Root Mean Square Error) values further confirm the differences in accuracy, with UAV-based models reaching as low as 0.71 m compared to over 1.0 m for ALS and DMR 5G. The results demonstrated that UAVs are well-suited for detailed analysis of rugged terrain morphology and obstacle identification during timber extraction, potentially replacing physical terrain surveys for timber extraction planning. Meanwhile, ALS and DMR 5G data showed significant potential for use in planning the placement of skidding trails and determining the direction and length of timber extraction from logging sites to forest roads, primarily due to their ability to cover large areas effectively. Differences in the analysis results obtained using GIS (Geographic Information System) cost surface solutions applied to ALS and DMR 5G data DTMs were evident on logging sites with terrain obstacles, where the site-specific ALS data proved to be more precise. While DMR 5G is based on ALS data, its generalised nature results in lower accuracy, making site-specific ALS data preferable for analysing rugged terrain and planning timber extractions. However, DMR 5G remains suitable for use in more uniform terrain without obstacles. Thus, we recommend combining UAV and ALS technologies for terrain with obstacles, as we found this approach optimal for efficiently planning the logging-transport process. Full article