Search Results (325)

Benthic habitats are highly complex and diverse ecosystems that are increasingly threatened by human-induced stressors and the impacts of climate change. Therefore, accurate classification and mapping of these marine habitats are essential for effective monitoring and management. In recent years, Unmanned Aerial Vehicles (UAVs) have been increasingly used to expand the spatial coverage of surveys and to produce high-resolution imagery. These images can be processed using photogrammetry-based techniques to generate high-resolution digital elevation models (DEMs) and orthomosaics. In this study, we demonstrate that integrating descriptors extracted from pre-trained Convolutional Neural Networks (CNNs) with geomorphometric attributes derived from DEMs significantly enhances the accuracy of automatic benthic habitat classification. To assess this integration, we analyzed orthomosaics and DEMs generated from UAV imagery across three shallow reef zones along the Red Sea coast of Saudi Arabia. Furthermore, we tested various combinations of feature layers from pre-trained CNNs—including ResNet-50, VGG16, and AlexNet—together with several geomorphometric variables to evaluate classification accuracy. The results showed that features extracted from the ResNet-50 FC1000 layer, when combined with twelve geomorphometric attributes based on curvature, slope, the Topographic Ruggedness Index (TRI), and DEM-derived heights, achieved the highest overall accuracies. Moreover, training a Support Vector Machine (SVM) classifier using both pre-trained ResNet-50 features and geomorphometric variables led to an improvement in overall accuracy of up to 5%, compared to using ResNet-50 features alone. The proposed integration effectively improves the automation and accuracy of benthic habitat mapping processes. Full article

Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality mesh models to enhance efficiency and accuracy in complex scenarios. The scene is segmented into buildings, vegetation, ground, and water bodies. Lightweight polygonal surfaces are extracted for buildings, while planar segments in non-building regions are fitted and projected into simplified polygonal patches. These photography targets are further decomposed into point, line, and surface primitives. A multi-resolution image acquisition strategy is adopted, featuring high-resolution coverage for buildings and rapid scanning for non-building areas. To ensure flight safety, a Digital Surface Model (DSM)-based shell model is utilized for obstacle avoidance, and sky-view-based Real-Time Kinematic (RTK) signal evaluation is applied to guide viewpoint optimization. Finally, a complete weighted graph is constructed, and ant colony optimization is employed to generate a low-energy-cost flight path. Experimental results demonstrate that, compared with traditional oblique photogrammetry, the proposed method achieves higher reconstruction quality. Compared with the commercial software Metashape, it reduces the number of images by 30.5% and energy consumption by 37.7%, while significantly improving reconstruction results in both architectural and non-architectural areas. Full article

Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB^® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector. Full article

The conservation of architectural heritage (AH) in regions threatened by natural and human-induced factors requires interdisciplinary approaches that integrate physical documentation with semantic modeling. This study introduces a comprehensive framework combining Historic Building Information Modeling (HBIM) with ontology-based modeling aligned with the CIDOC Conceptual Reference Model (CIDOC CRM). Focusing on the Big Gate and adjacent curtain walls in Ibb, Yemen, where the gate is entirely lost, the study reconstructs the structure using historical photographs, eyewitness accounts, and analogical references. The methodology incorporates UAV and terrestrial photogrammetry surveys, point cloud generation, and semantic enrichment using Autodesk Revit V. 2024 and Protégé V. 5.5. Decay phenomena such as cracks, efflorescence, and disintegration were ontologically classified and spatially linked to the HBIM model, revealing deterioration patterns concerning historical phases and environmental exposure. The resulting system enables dynamic documentation, facilitates strategic conservation planning, and enhances data interoperability across heritage platforms. The proposed framework is transferable to other heritage sites, supporting both the conservation of extant structures and the reconstruction of lost ones. Full article

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

The safety and longevity of engineering structures depend on precise and timely monitoring, especially during load testing inspections. Conventional displacement measurement methods—such as LVDT sensors, GNSS, RTS, and levels—each present benefits and limitations in terms of accuracy, applicability, and practicality. Photogrammetry has emerged as a promising alternative, offering non-contact measurement, cost-effectiveness, and adaptability in challenging environments. This study investigates the potential of photogrammetric methods for determining structural displacements during load testing in real-world conditions where such approaches remain underutilized. Two photogrammetric techniques were tested: (1) a single-image homography-based approach, and (2) a multi-image bundle block adjustment (BBA) approach using both UAV and tripod-mounted imaging platforms. Displacement results from both methods were compared against reference measurements obtained by traditional LVDT sensors and robotic total station. The study evaluates the influence of different camera systems, image acquisition techniques, and processing methods on the overall measurement accuracy. The findings suggest that the photogrammetric method, especially when optimized, can provide reliable displacement data with sub-millimeter accuracy, highlighting their potential as a viable alternative or complement to established geodetic and sensor-based approaches in structural testing. Full article

Mangrove forests are vital blue carbon (BC) ecosystems that significantly contribute to climate change mitigation through carbon sequestration. Accurate, scalable, and cost-effective methods for estimating carbon stocks in these environments are essential for conservation planning. In this study, we assessed the potential of drones, also known as unmanned aerial vehicles (UAVs), for estimating above-ground biomass (AGB) and BC in Avicennia marina stands by integrating drone-based canopy measurements with field-measured tree heights. Using structure-from-motion (SfM) photogrammetry and a consumer-grade drone, we generated a canopy height model and extracted structural parameters from individual trees in the Melgonze mangrove patch, southern Iran. Field-measured tree heights served to validate drone-derived estimates and calibrate an allometric model tailored for A. marina. While drone-based heights differed significantly from field measurements (p < 0.001), the resulting AGB and BC estimates showed no significant difference (p > 0.05), demonstrating that crown area (CA) and model formulation effectively compensate for height inaccuracies. This study confirms that drones can provide reliable estimates of BC through non-invasive means—eliminating the need to harvest, cut, or physically disturb individual trees—supporting their application in mangrove monitoring and ecosystem service assessments, even under challenging field conditions. Full article

Civil structures carry significant service loads over long times but are prone to deterioration due to various natural impacts. Traditionally, these structures are inspected in situ by qualified engineers, a method that is high-cost, risky, time-consuming, and prone to error. Recently, researchers have explored innovative practices by using virtual reality (VR) technologies as inspection platforms. Despite such efforts, a critical question remains: can VR models accurately reflect real-world structural conditions? This study presents a comprehensive framework for assessing the visual fidelity of VR models for structural inspection. To make it viable, we first introduce a novel workflow that integrates UAV-based photogrammetry, computer graphics, and web-based VR editing to establish interactive VR user interfaces. We then propose a visual fidelity assessment methodology that quantitatively evaluates the accuracy of the VR models through image alignment, histogram matching, and pixel-level deviation mapping between rendered images from the VR models and UAV-captured images under matched viewpoints. The proposed frameworks are validated using two case studies: a historic stone arch bridge and a campus steel building. Overall, this study contributes to the growing body of knowledge on VR-based structural inspections, providing a foundation for our peers for their further research in this field. Full article

Significant morphological transformations resulting from open-pit mining activities always present major problems with site safety and slope stability. This study investigates an active marble quarry in Dinar, Türkiye by combining geospatial analysis and photogrammetry based on unmanned aerial vehicles (UAV). Acquired in 2024 and 2025, high-resolution images were combined with dense point clouds produced by Structure from Motion (SfM) methods. Iterative Closest Point (ICP) registration (RMSE = 2.09 cm) and Multiscale Model-to-Model Cloud Comparison (M3C2) analysis was used to quantify the surface changes. The study found a volumetric increase of 7744.04 m³ in the dump zones accompanied by an excavation loss of 8359.72 m³, so producing a net difference of almost 615.68 m³. Surface risk factors were evaluated holistically using a variety of morphometric criteria. These measures covered surface variation in several respects: their degree of homogeneity, presence of any unevenness or texture, verticality, planarity, and linearity. Surface variation > 0.20, roughness > 0.15, and verticality > 0.25 help one to identify zones of increased instability. Point cloud modeling derived from UAVs and GIS-based spatial analysis were integrated to show that morphological anomalies are spatially correlated with possible failure zones. Full article

Agricultural terraces are critical landscape elements that promote sustainable rural development by enhancing water retention, mitigating soil erosion, and conserving cultural heritage. In northeastern Türkiye, particularly in the mountainous Erikli neighborhood of Uzundere, traditional terraces face growing threats due to land abandonment, topographic fragility, and socio–economic decline. This study applies a spatial–functional assessment framework that integrates UAV–based photogrammetry, GIS analysis, terrain modeling, and DBSCAN clustering to evaluate terrace conditions. UAVs provided high–resolution topographic data, which supported the delineation of terrace boundaries and morphometric classification using an adapted ALPTER model. A combined Terrace Density Index (TDI) and Functional Status Index (FSI) approach identified zones where terraces are structurally intact but functionally degraded. Results indicate that 76.4% of terraces fall within the meso and macro classes, yet 58% show partial or complete degradation. Cohesive terrace clusters are located near settlements, while isolated units in peripheral zones display higher vulnerability. This integrated approach demonstrates the analytical potential of drone–supported spatial diagnostics for monitoring landscape degradation. The method is scalable and adaptable to other terraced regions, offering practical tools for site–specific land use planning, heritage conservation, and resilience–based restoration strategies. Full article

Coarse-grained beaches consisting of gravel, pebbles, and cobbles play a crucial role in coastal protection. On the Croatian Adriatic coast, there are artificial gravel pocket beaches created for recreational and protective purposes. However, these beaches are subject to constant morphological changes due to natural forces and human intervention. This study investigates the morphodynamics of artificial gravel pocket beaches, focusing on berm formation and crest build-up processes characteristic for low to moderate wave conditions. Despite mimicking natural formations, artificial beaches require regular maintenance due to sediment shifts dominantly caused by wave action and storm surges. Structure-from-Motion (SfM) photogrammetry and UAV-based surveys were used to monitor morphological changes on the artificial gravel pocket beach Ploče (City of Rijeka). The XBeach-Gravel model, originally adapted to simulate the effects of high-energy waves, was calibrated and validated to analyze low to moderate wave dynamics on gravel pocket beaches. The calibration includes adjustments to the inertia coefficient (ci), which influences sediment transport by shear stress at the bottom; the angle of repose (ϕ), which controls avalanching and influences sediment transport on sloping beds; and the bedload transport calibration coefficient (γ), which scales the transport rates linearly. By calibrating XBeach-G for low to moderate wave conditions, this research improves the accuracy of the model for the cases of morphological responses “berm formation” and “crest build-up”. Full article

This study presents an integrated surveying methodology for efficient and accurate cadastral documentation, combining UAV photogrammetry, SLAM-based terrestrial and aerial scanning, and conventional geodetic measurements. Designed to be scalable across various cadastral and planning contexts, the workflow was tested in Charlottenburg, Romania’s only circular heritage village. The approach addresses challenges in built environments where traditional total station or GNSS techniques face limitations due to obstructed visibility and complex architectural geometries. The SLAM system was initially deployed in mobile scanning mode using a backpack configuration for ground-level data acquisition, and was later mounted on a UAV to capture building sides and areas inaccessible from the main road. The results demonstrate that the integration of aerial and terrestrial data acquisition enables precise building footprint extraction, with a reported RMSE of 0.109 m between the extracted contours and ground-truth total station measurements. The final cadastral outputs are fully compatible with GIS and CAD systems, supporting efficient land registration, urban planning, and historical site documentation. The findings highlight the method’s applicability for modernizing cadastral workflows, particularly in dense or irregularly structured areas, offering a practical, accurate, and time-saving solution adaptable to both national and international land administration needs. Beyond the combination of known technologies, the innovation lies in the practical integration of terrestrial and aerial SLAM (dual SLAM) with RTK UAV workflows under real-world constraints, offering a field-validated solution for complex cadastral scenarios where traditional methods are limited. Full article

Amid intensifying climatic threats, university campuses are increasingly vulnerable. Morphological resilience offers a practical pathway to strengthen disaster response in higher-education institutions. However, research on University Campus Morphological Resilience (UCMR) remains underexplored, with gaps in theory, quantitative methodology, and empirical application. The study established a theoretical framework and an assessment system for UCMR, focusing on four core resilience attributes—robustness, redundancy, connectivity, and diversity—in three common disaster scenarios: earthquakes, flooding, and extreme heat. The Weijinlu (WJL) and Beiyangyuan (BYY) campuses of Tianjin University were selected as case studies. We used Unmanned Aerial Vehicle (UAV) photogrammetry to collect morphological data at a high spatial resolution of 0.1 m. UCMR was evaluated for each disaster scenario, followed by a multi-scenario cluster coupling analysis. The results indicate that, first, the WJL Campus exhibited a lower overall UCMR across various disaster scenarios compared to the BYY Campus, particularly during earthquakes and flooding, with less pronounced differences observed under extreme heat. Second, both campuses demonstrate significant spatial heterogeneity in UCMR across three disaster scenarios. Third, the WJL Campus performs better in redundancy and diversity but worse in connectivity, with lower robustness under earthquakes and flooding, and higher robustness under extreme heat. Fourth, UCMR in BYY Campus displayed consistent spatial patterns characterized by high-resilience clusters, while UCMR in WJL Campus presented greater variability across the three disaster scenarios, showcasing complex multi-scenario cluster types and spatial fragmentation. Based on the above findings, we developed tailored UCMR optimization strategies. The study offers a scientific reference for resilience-oriented campus planning and disaster risk management. Full article

Nighttime terrain mapping has remained a significant challenge in photogrammetry due to the absence of visible light required by conventional imaging systems. This study evaluates the feasibility of generating Digital Surface Models (DSMs) from nighttime aerial thermal imagery using structure-from-motion photogrammetry. A DJI Mavic 3 Enterprise Thermal Unmanned Aerial Vehicle (UAV) captured 1746 images at 35 m altitude over a 9.4-hectare campus environment. Reflective aluminum sheets served as ground control points, ensuring visibility in thermal imagery under nocturnal conditions. The resulting thermal DSM achieved a point density of 0.117 points/cm². Statistical analysis of four independent checkpoints yielded a root mean square error (RMSE) of 0.0522 m, a mean error (ME) of −0.052 m, and a standard deviation (SD) of 0.0054 m, indicating high vertical accuracy with minimal scatter around the systematic bias. Comparison with a reference RGB-based DSM revealed a correlation coefficient of 0.975, demonstrating strong spatial agreement. These results establish that high-quality DSMs can be generated solely from nighttime thermal imagery, providing a viable alternative for applications requiring 24-h operational capability, including emergency response, post-disaster assessment, and nocturnal environmental monitoring where traditional photogrammetry is impractical. Full article

Intrinsic parameter estimation by self-calibration is commonly used in Unmanned aerial vehicle (UAV)-based photogrammetry with Structure from Motion (SfM). However, obtaining stable estimates of these parameters from image-based SfM—which relies solely on images, without auxiliary data such as ground control points (GCPs)—remains challenging. Aerial imagery acquired with the constant-pitch (CP) flight pattern often exhibits non-linear deformations, highly unstable intrinsic parameters, and even alignment failures. We hypothesize that CP flights form a “critical configuration” that renders certain intrinsic parameters indeterminate. Through numerical experiments, we confirm that a CP flight configuration does not provide sufficient constraints to estimate focal length (f) and the principal point coordinate (cy) in image-based SfM. Real-world CP datasets further demonstrate the pronounced instability of these parameters. As a remedy, we show that by introducing intermediate strips into the CP flight plan—what we call a CP-Plus flight—can effectively mitigate the indeterminacy of f and cy in simulations and markedly improve their stability in all tested cases. This approach enables more effective image-only SfM workflows without auxiliary data, simplifies data acquisition, and improves three-dimensional reconstruction accuracy. Full article