Search Results (314)

Low-altitude drones, which are unimpeded by traffic congestion or urban terrain, have become a critical asset in emergency rescue missions. To address the current lack of emergency rescue data, UAV aerial videos were collected to create an experimental dataset for action classification and localization annotation. A total of 5082 keyframes were labeled with 1–5 targets each, and 14,412 instances of data were prepared (including flight altitude and camera angles) for action classification and position annotation. To mitigate the challenges posed by high-resolution drone footage with excessive redundant information, we propose the SlowFast-Traffic (SF-T) framework, a spatio-temporal sequence-based algorithm for recognizing traffic accident rescue actions. For more efficient extraction of target–background correlation features, we introduce the Actor-Centric Relation Network (ACRN) module, which employs temporal max pooling to enhance the time-dimensional features of static backgrounds, significantly reducing redundancy-induced interference. Additionally, smaller ROI feature map outputs are adopted to boost computational speed. To tackle class imbalance in incident samples, we integrate a Class-Balanced Focal Loss (CB-Focal Loss) function, effectively resolving rare-action recognition in specific rescue scenarios. We replace the original Faster R-CNN with YOLOX-s to improve the target detection rate. On our proposed dataset, the SF-T model achieves a mean average precision (mAP) of 83.9%, which is 8.5% higher than that of the standard SlowFast architecture while maintaining a processing speed of 34.9 tasks/s. Both accuracy-related metrics and computational efficiency are substantially improved. The proposed method demonstrates strong robustness and real-time analysis capabilities for modern traffic rescue action recognition. 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

Field data, as the basis for planning and designing forest roads, must have high spatial accuracy. Classical (using a theodolite and a level) and modern (based on total stations and GNSSs) surveying methods are used in current field data survey for forest road design. This study analyzed the spatial accuracy of classical and modern surveying methods, the accuracy of spatial data recorded using a UAV equipped with an RGB camera at different flight altitudes, and the accuracy of lidar data of the Republic of Croatia. This study was conducted on a forest area where salvage logging was carried out, which enabled the use of a GNSS receiver in RTK mode as a reference method. The highest RMSE values of the spatial coordinates were recorded for measurements obtained with the classical surveying method (0.89 m) and a total station (0.33 m). The flight altitude of the UAV did not significantly affect the spatial error of the collected data, which ranged between 0.07 and 0.09 m. The cross-terrain slope, as one of the factors that significantly affect the amount of earthworks, did not differ statistically significantly between the methods. The ALS error was strongly influenced by the cross-terrain slope. The authors conclude that the new survey methods (SfM and lidar data) provide high-accuracy data but also draw attention to challenges in their use, such as vegetation and biomass on the ground. 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

This study aims to address the critical lack of standardized guidelines for unmanned aerial vehicle (UAV) image acquisition strategies utilizing structure-from-motion (SfM) by focusing on 3D building exterior modeling. A comprehensive experimental analysis was conducted to systematically investigate and quantitatively evaluate the effects of various shooting patterns and parameters on SfM reconstruction quality and processing efficiency. This study implemented a systematic experimental framework to test various UAV flight patterns, including circular, surface, and aerial configurations. Under controlled environmental conditions on representative building structures, key variables were manipulated, and all collected data were processed through a consistent SfM pipeline based on the SIFT algorithm. Quantitative evaluation results using various analytical methodologies (multiple regression analysis, Kruskal–Wallis test, random forest feature importance, principal component analysis including K-means clustering, response surface methodology (RSM), preference ranking technique based on similarity to the ideal solution (TOPSIS), and Pareto optimization) revealed that the basic shooting pattern ‘type’ has a significant and statistically significant influence on all major SfM performance metrics (reprojection error, final point count, computation time, reconstruction completeness; Kruskal–Wallis p < 0.001). Additionally, within the patterns, clear parameter sensitivity and complex nonlinear relationships were identified (e.g., overlapping variables play a decisive role in determining the point count and completeness of surface patterns, with an adjusted R² ≈ 0.70; the results of circular patterns are strongly influenced by the interaction between radius and tilt angle on reprojection error and point count, with an adjusted R² ≈ 0.80). Furthermore, composite pattern analysis using TOPSIS identified excellent combinations that balanced multiple criteria, and Pareto optimization explicitly quantified the inherent trade-offs between conflicting objectives (e.g., time vs. accuracy, number of points vs. completeness). In conclusion, this study clearly demonstrates that hierarchical strategic approaches are essential for optimizing UAV-SfM data collection. Additionally, it provides important empirical data, a validated methodological framework, and specific quantitative guidelines for standardizing UAV data collection workflows, thereby improving existing empirical or case-specific approaches. 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

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

As transportation infrastructure systems continue to expand, the demand for unmanned aerial vehicle (UAV) technologies in the assessment of urban infrastructure is expected to grow substantially, due to their strong potential for efficient data collection and post-processing. UAVs offer numerous advantages in infrastructure assessment, including enhanced time and cost efficiency, improved safety, and the ability to capture high-quality data. Furthermore, integrating various data-collecting sensors enhances the versatility of UAVs, enabling the acquisition of diverse data types to support comprehensive infrastructure evaluations. Numerous post-processing software applications utilizing various structure-from-motion (SfM) techniques have been developed, significantly facilitating the assessment process. However, researchers’ efforts to find the potentialities of this technology will be in vain if its applications are not utilized effectively in the practical field. Therefore, this study aims to determine the adaptation condition of UAV technologies in different Department of Transportation (DOT) and Federal Highway Administration (FHWA) agencies to assess transportation infrastructure systems. This study also explores the quantitative analysis of benefits and challenges/barriers, expectations for every UAV and post-processing software, and the cutting-edge features that should be integrated with UAVs to effectively evaluate transportation infrastructure systems. A comprehensive survey form was distributed to all 50 DOTs and the FHWA, and 35 complete responses were recorded from 27 DOTs and the FHWA. The survey results show that 25 agencies currently use UAVs for roads or highways, and 23 DOTs for bridges, confirming these as the most commonly assessed infrastructure systems. The top benefits found in this study include safety, cost effectiveness, and time efficiency (mean ratings: 3.95–4.28), while weather, FAA regulations, and airspace restrictions are the main challenges. Respondents emphasize the need for longer flight times, better automation, and advanced data tools, underscoring growing adoption and highlighting the need to overcome technical, regulatory, and data privacy challenges for optimal UAV integration within transportation infrastructure systems management. 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

This study explores the growing application of 3D remote sensing in geohazard studies, particularly for rock slope monitoring. It highlights the use of cost-effective Street View Imagery (SVI) and Unmanned Aerial Vehicles (UAV) through Structure-from-Motion (SfM) photogrammetry as tools for 3D rockfall monitoring. Using multi-temporal SVI and UAV Imagery from the Koto Panjang cliff in Indonesia, we quantify rockfall volume changes over seven years and assess associated geohazards. The results reveal a total rockfall retreat of 5270 m³, with an average annual rate of 7.53 m³/year. Structural analysis identified six major discontinuity sets and confirmed inherent instability within the rock mass. Kinematic simulations using SVI and UAV-derived data further assessed rockfall trajectories and potential impact zones. Results indicate that 40% of simulated rockfall deposits accumulated near existing roads, with significant differences in distribution based on scree slope angles. This emphasizes the role of scree slope in influencing rockfall propagation. In conclusion, SVI and UAV imagery presents a valuable tool for 3D point cloud reconstruction and rockfall hazard assessment, particularly in areas lacking historical data. The study showcases the effectiveness of using SVI and UAV imagery in quantifying historical past rockfall volume and identifies critical areas for mitigation strategies, highlighting the importance of scree slope angle in managing rockfall hazard. Full article

The structure from motion (SfM) and multiview stereo (MVS) techniques have proven effective in generating high-quality 3D point clouds, particularly when integrated with unmanned aerial vehicles (UAVs). However, the impact of image quality—a critical factor for SfM–MVS techniques—has received limited attention. This study proposes a method for optimizing camera settings and UAV flight methods to minimize point cloud errors under illumination and time constraints. The effectiveness of the optimized settings was validated by comparing point clouds generated under these conditions with those obtained using arbitrary settings. The evaluation involved measuring point-to-point error levels for an indoor target and analyzing the standard deviation of cloud-to-mesh (C2M) and multiscale model-to-model cloud comparison (M3C2) distances across six joint planes of a rock mass outcrop in Seoul, Republic of Korea. The results showed that optimal settings improved accuracy without requiring additional lighting or extended survey time. Furthermore, we assessed the performance of SfM–MVS under optimized settings in an underground tunnel in Yeoju-si, Republic of Korea, comparing the resulting 3D models with those generated using Light Detection and Ranging (LiDAR). Despite challenging lighting conditions and time constraints, the results suggest that SfM–MVS with optimized settings has the potential to produce 3D models with higher accuracy and resolution at a lower cost than LiDAR in such environments. Full article

This research presents a methodological approach combining UAV-LiDAR technology and SfM photogrammetry for the comprehensive documentation and analysis of O Corro dos Mouros, a Bronze-to-Iron Age archaeological site in the northwest of the Iberian Peninsula. The study evaluates both the capabilities and limitations of this integrated approach, focusing on a recently identified Roda-type structure, characterised by circular stone architecture and funerary-ritual functionality, dating between the 15th and 3rd centuries BC. The methodology combines RTK-corrected LiDAR (150 pts/m², ±5 cm accuracy) with 20.4 MP RGB imaging, overcoming vegetation cover while capturing surface details. The results demonstrate the superior performance of the proposed methodology compared to public LiDAR (1 m resolution), offering more detailed and precise microtopographic data of the circular structure. The approach successfully addresses three key challenges: (1) dense vegetation penetration, (2) multi-phase stratigraphic documentation, and (3) non-invasive monitoring of sensitive sites. The centimetre-accurate 3D models (publicly available via Sketchfab) provide both research-grade data for analysing construction phases and contextual relationships with nearby rock art/megaliths, and engaging visualisations for heritage interpretation. This work establishes a replicable technical framework optimised for high-resolution archaeological documentation, with direct applicability to similar ritual landscapes (hillforts, burial mounds) across the region. Full article

Point clouds continue to be the main data source in 3D modeling studies with unmanned aerial vehicle (UAV) images. Structure-from-Motion (SfM) and MultiView Stereo (MVS) have high time costs for point cloud generation, especially in large data sets. For this reason, state-of-the-art methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have emerged as powerful alternatives for point cloud generation. This paper explores the performance of NeRF and 3DGS methods in generating point clouds from UAV images. For this purpose, the Nerfacto, Instant-NGP, and Splatfacto methods developed in the Nerfstudio framework were used. The obtained point clouds were evaluated by taking the point cloud produced with the photogrammetric method as reference. In this study, the effects of image size and iteration number on the performance of the algorithms were investigated in two different study areas. According to the results, Splatfacto demonstrates promising capabilities in addressing challenges related to scene complexity, rendering efficiency, and accuracy in UAV imagery. Full article

Deadwood is a vital component of forest ecosystems, significantly contributing to biodiversity and carbon storage. Accurate mapping of deadwood is essential for ecological monitoring and sustainable forest management. This study introduces a method for downed deadwood mapping using a convolutional neural network (CNN) applied to very high-resolution UAV RGB imagery. The research was conducted in Hainich National Park, central Germany, aiming to enhance the precision of coarse woody debris (CWD) delineation in a dense and structurally diverse temperate deciduous forest. Key objectives included testing the deep learning (DL) model’s performance at area, length, and object levels and benchmarking its accuracy against a traditional object-based image analysis (OBIA) method. Deadwood volume was calculated from the mapping results. By implementing a U-Net architecture with a ResNet-34 backbone and utilizing data augmentation techniques, the model achieved very high classification performance (F1-scores between 73% and 96%). It provided precise delineation of individual CWD objects from the underlying ground, representing detailed stem forms. High precision values highlight the reliability of the mapping results, while lower recall values indicate that some CWD objects, especially smaller branches, were missed. The DL approach achieved higher accuracy values across all testing methods compared to the OBIA method. The study also addresses the challenges posed by spectral ambiguities in decomposed deadwood and recommends future research directions for enhancing model generalization across diverse forest types and acquisition conditions. Full article

Previous studies have shown that the use of appropriate ground control points (GCPs) and camera calibration models can optimize photogrammetry. However, the synergistic effects of GCPs and camera calibration models on UAV-SfM photogrammetry are still unknown. This study used camera models with varying complexities under different GCP conditions (in terms of number and quality) for UAV-SfM photogrammetry. The correlation matrix and root mean squared error (RMSE) were used to analyze the synergistic effects of GCPs and camera models. The results show that (1) without GCPs, complex camera models reduce distortion parameter correlation and improve terrain modeling accuracy by about 70%, with Model C (with F, Cx, Cy, K1–K4, and P1–P4) being the most widely applicable. (2) Increasing the number of GCPs enhances the terrain modeling accuracy more effectively than increasing the camera model complexity, reducing the RMSE by 45–70%, while the model complexity does not affect the required GCP number. (3) A strong interaction exists between the GCP quality and camera models: High-quality GCPs enhance camera model performance, while complex camera models reduce the requirement of GCP quality. This study provides both theoretical insights and practical guidance for efficient and low-cost UAV-SfM photogrammetry in different scenarios. Full article