Photogrammetry Techniques Research Articles

Prediction method of surface subsidence induced by block caving method based on UAV oblique photogrammetry

Unmanned Aerial Vehicle (UAV) oblique photogrammetry has been extensively employed in mining, albeit predominantly for reconstructing three-dimensional scenes and detecting changes within mining sites, lacking predictive capabilities. Leveraging 3D real scene model data, this study presents a two-stage prediction model, merging the probabilistic integral method with recurrent neural network (PIMF-RNN), to mitigate the impact of internal and external factors on surface subsidence, thereby enhancing predictive accuracy. Building upon this framework, a methodology was developed to forecast the maximum surface subsidence height and affected area under the block caving method, offering crucial data support for mitigating hazards associated with this mining technique. Analysis of surface data from Pulang copper mine during 2018–2020 demonstrates a prediction accuracy of 91.47% for maximum surface subsidence height and 87.52% for subsidence area. This research expands the potential applications of UAV oblique photogrammetry techniques within mining contexts. Furthermore, it establishes a cost-effective and efficient operational procedure for predicting mine surface subsidence.

Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV

Abstract This study evaluated seven vegetation indices for the monitoring of a cherry tomato crop using an unmanned aerial vehicle with a multispectral camera that measures in the green, red, and near-infrared spectral bands. A photogrammetric flight plan was designed to capture the spectral images every 2 weeks in two agricultural parcels identified as Treatment 1 ( T 1 {T}_{1} ) and Treatment 2 ( T 2 {T}_{2} ). The corresponding orthophotographs were obtained using digital photogrammetry techniques. Subsequently, vegetation indices were calculated for these orthophotographs. The mean and standard deviation of these indices were extracted, and a statistical analysis was performed to compare the vegetation indices and to analyze their behavior over time. Analysis of variance showed that the ratio vegetation index (RVI), green vegetation index (GVI), normalized difference vegetation index (NDVI), infrared percentage vegetation index (IPVI), green normalized difference vegetation index (GNDVI), and optimized soil-adjusted vegetation index (OSAVI) indices showed significant variation (P-value <0.05) over time. No statistically significant differences between the two treatments were found. IPVI, NDVI, and OSAVI showed less variation in pixel values. RVI, GVI, NDVI, IPVI, GNDVI, and OSAVI proved to be valuable tools for monitoring field crops since these indices responded to the crop growth kinetics.

Predicting 3D and 2D surface area of corals from simple field measurements

The structural architecture of coral reefs is a known predictor of species richness, fish biomass and reef resilience. At a smaller scale, three-dimensional (3D) surface area of corals is a fundamental determinant of physical and biological processes. Quantifying the 3D surface area of corals has applications for a broad range of scientific disciplines, including carbonate production estimates, coral predation studies, and assessments of reef growth. Here, we present morphotaxon-specific conversion metrics to estimate total 3D surface area and projected 2D surface area of individual colonies from simple field measurements of colony maximum diameter. Underwater photogrammetry techniques were used to quantify surface area and estimate conversion metrics. Bayesian models showed strong non-linear (power) relationships between colony maximum diameter and both total 3D surface area and projected 2D surface area for 13 out of 15 morphotaxa. This study presents a highly resolved and efficient method for obtaining critical surface area assessments of corals for various applications, including assessments of biotic surface area, tissue biomass, calcification rates, coral demographic rates, and reef restoration monitoring.

Exploring mouse necropsy through augmented reality: developing a web application for enhanced learning and visualization

Necropsy, the examination of animal carcasses to determine the cause of death, is an essential skill for many professionals. Traditional training methods, however, are costly and time-consuming. The article suggests that Web-based Augmented Reality (WebAR) can offer an immersive and cost-effective training experience for laboratory animal necropsy. It describes using photogrammetry techniques to create a virtual necropsy environment consisting of 10 necropsy steps. A questionnaire was used to evaluate the usability, educational value, and drawbacks of the designed application by students who tested it. The paper outlines best practices for developing WebAR simulations, including high-fidelity 3D models and interactive elements. Additionally, it presents methods for creating new WebAR applications using specific programs or scripts. This paper highlights the potential benefits of WebAR for laboratory animal necropsy training, emphasizing its accessibility, cost-effectiveness, and scalability.

Methods and research for deformation monitoring of earth and rock dams based on close-range photogrammetry

Abstract Traditional means of monitoring deformation in earth and rock dams encounter challenges such as low monitoring efficiency and limited coverage. Despite the potential of emerging technologies such as GPS and three-dimensional laser scanning, their adoption is expensive and hard to promote. This paper presents a deformation monitoring method for earth and rock dams based on the close-range photogrammetry technique. The proposed approach focuses on analytical algorithm the design and deployment of monitoring points, photographic schemes, camera checking and calibration, as well as deformation analysis methods. Initially, based on the analysis of the parsing algorithms’ applicability, they are fused to address the shortcomings of common image parsing methods in meeting the requirements of high precision and multi-image processing for deformation monitoring of earth and rock dams. Subsequently, the fused algorithm is introduced to analyze the acquired image data for 3D reconstruction, and the deformation in earth and rock dams is assessed based on the generated dense point cloud model. The proposed deformation monitoring method is applied to Pine Bridge Reservoir Dam, and the results demonstrated its capacity to comprehensively analyze the deformation. Furthermore, the required equipment is simple and easy to operate, aligning with the requirements for deformation monitoring accuracy of earth and rock dams.

Foothold selection during locomotion in uneven terrain: Results from the integration of eye tracking, motion capture, and photogrammetry.

Relatively little is known about the way vision is used to guide locomotion in the natural world. What visual features are used to choose paths in natural complex terrain? To answer this question, we measured eye and body movements while participants walked in natural outdoor environments. We incorporated measurements of the 3D terrain structure into our analyses and reconstructed the terrain along the walker's path, applying photogrammetry techniques to the eyetracker's scene camera videos. Combining these reconstructions with the walker's body movements, we demonstrate that walkers take terrain structure into account when selecting paths through an environment. We find that they change direction to avoid taking steeper steps that involve large height changes, instead of choosing more circuitous, relatively flat paths. Our data suggest walkers plan the location of individual footholds and plan ahead to select flatter paths. These results provide evidence that locomotor behavior in natural environments is controlled by decision mechanisms that account for multiple factors, including sensory and motor information, costs, and path planning.

Decoding urban evolution: A parametric reconstruction of the Old City of Aleppo between the mamluk and the post-war city

The Old City of Aleppo suffered extensive urban damage during the war in Syria. This study showcases the implementation of digital pipelines in visualizing and studying the history and evolution of two districts within the Old City. The aim of this study was to develop a parametric workflow that would allow for the reconstruction of a section of Mamluk Aleppo. This reconstruction was based on both cartographical and historical inputs. A comprehensive digital field survey was conducted using laser scanning and aerial photogrammetry techniques. The resulting point cloud data is then compared to the historical parametric model, and 3D urban changes to the studied area were identified.

Photogrammetry scans for neuroanatomy education-a new multi-camera system: technical note.

Photogrammetry scans has directed attention to the development of advanced camera systems to improve the creation of three-dimensional (3D) models, especially for educational and medical-related purposes. This could be a potential cost-effective method for neuroanatomy education, especially when access to laboratory-based learning is limited. The aim of this study was to describe a new photogrammetry system based on a 5 Digital Single-Lens Reflex (DSLR) cameras setup to optimize accuracy of neuroanatomical 3D models. One formalin-fixed brain and specimen and one dry skull were used for dissections and scanning using the photogrammetry technique. After each dissection, the specimens were placed inside a new MedCreator® scanner (MedReality, Thyng, Chicago, IL) to be scanned with the final 3D model being displayed on SketchFab® (Epic, Cary, NC) and MedReality® platforms. The scanner consisted of 5 cameras arranged vertically facing the specimen, which was positioned on a platform in the center of the scanner. The new multi-camera system contains automated software packages, which allowed for quick rendering and creation of a high-quality 3D models. Following uploading the 3D models to the SketchFab® and MedReality® platforms for display, the models can be freely manipulated in various angles and magnifications in any devices free of charge for users. Therefore, photogrammetry scans with this new multi-camera system have the potential to enhance the accuracy and resolution of the 3D models, along with shortening creation time of the models. This system can serve as an important tool to optimize neuroanatomy education and ultimately, improve patient outcomes.

Estimating body condition of Apennine brown bears using subjective scoring based on camera trap photographs

The assessment of animal body condition has important practical and management implications for endangered wildlife populations. The nutritional condition of a population can be evaluated in a non-invasive way using photogrammetry techniques, avoiding direct manipulation. This study evaluates the utility of using body condition scoring (BCS) based on the visual assessment of subcutaneous fat and muscle from the body contour as a non-invasive method to quantify body condition in free ranging bears from camera trap photographs. Photographs of Apennine brown bears (Ursus arctos marsicanus), taken between 2007 and 2009 in the Abruzzo, Lazio and Molise National Park (PNALM, Italy), were used to evaluate the potential of this technique. BCS assessment was performed on 754 photographs representing 71 independent observations. Forty-eight of these photographs were selected to also score quantitative body ratios using a standardised measure of torso height. BCS varied seasonally, as expected by food availability and brown bear nutritional physiology, and it was also positively correlated to all three body ratios. Our findings indicate that BCS assessment is a good proxy for body condition, and that camera trap data can be effectively used to assess and monitor the nutritional condition of bear populations, such as the critically endangered one in central Italy.

Enhancing Archaeological Research Through Immersive Virtual Reality.

Virtual reality (VR) is increasingly employed in archaeology to showcase reconstructions of ancient sites to the general public, yet its utilization for professional purposes by archaeologists remains less common. To address this gap, we introduce a VR application specifically designed to streamline the storage and access of critical data for archaeological studies. This application provides experts with an immersive visualization of excavation sites and related information during the postexcavation analysis phase. The application interface facilitates direct interaction with 3-D models generated through photogrammetry and modeling techniques, enabling detailed examination of collected data and enhancing research activities. We applied this system to the case study of excavations at the Temple of Juno in Agrigento, Italy. In addition, we present the findings of a pilot user study involving archaeologists, which evaluates the effectiveness of immersive technologies for professionals in documenting, preserving, and exploring archaeological sites, while also driving potential future developments.

Digital Fracture Porosity Measurement using Digital Outcrop Modelling (DOM)

Abstract Fracture measurements for porosity calculations often experience problems such as barely accessible locations, limited area coverage, and a long time to carry out field data measurements. Therefore, refined alternative methods are necessary to obtain the most favourable data. This study demonstrates fracture measurements through application of Digital Outcrop Modelling (DOM) using the Structure from Motion (SfM) method to utilize digital photogrammetry techniques. This method was chosen because data collection can be done remotely using Light Detection and Ranging (LiDAR) or digital photogrammetry to obtain point cloud data that is used to perform three-dimensional outcrop modeling. In addition, SfM also produces high-resolution data without the need for experts, is cost-efficient and safer than other methods. Hand-held platform Canon 700D digital single lens reflex (DSLR) camera was used in this study to capture data from an area of a square meter located on the riverbed that requires detailed modeling. DOM using SfM is applied to conduct aperture measurements in fractures inside the scan window, later used as data for calculating basement fracture porosity predictions. Field data collection is also carried out to validate data generated by DOM. Calculations achieved from field data and DOM data will be compared to determine differences of the porosity prediction values. Field data collection was carried out on 3 scan windows, each one is an area of 1 m2 and in totals obtained 117 fracture data, consist of 44 extension joints, 34 release joints, 7 shear joints, and 32 non-systematic joints. In this study, 115 photos were taken from scanwindow 1, 162 photos from scanwindow 2, and 158 photos from scanwindow 3 to construct the DOM using agisoft metashape software. The difference in these values can be used as a correction formula or evaluation for further research. The results of measuring basement fracture porosity using data generated by DOM will remain in the range of less than 1%.

Петроглифы Канозера: новые фигуры панели Каменный 4

The paper presents new documentation of the Kamenny 4 panel with petroglyphs of the Kanozero rock art complex. Drawings of petroglyphs and microrelief elements are made based on 3D model obtained with the help of the photogrammetry technique. Refinements of known figures and revealed new images are necessary for detailed compositional analysis of the cluster and hypotheses on interpreting this composition of petroglyphs.

Designing and evaluation of a mixed reality system for crime scene investigation training: a hybrid approach

Police investigation in real-life crime scenes is an essential aspect of forensic science education. However, the practicality of bringing young investigators to actual crime scenes is often hindered by the costs and challenges involved. In order to overcome these obstacles, new technologies such as mixed reality (MR) are being explored as potential solutions. MR technology offers an interactive and cost-effective way to simulate real-life crime scenes, providing a valuable training experience for young investigators. This paper presents a novel design of a MR system using Microsoft HoloLens 2.0, which is tailored to work in a spatial 3D scanned and reconstructed crime scene using FARO point cloud 3D scanner X130 blended with photogrammetry techniques. The system was developed through the lens of Experiential Learning Theory and designed using a participatory approach, providing a cost-effective solution to help trained Kuwaiti police officers enhance their investigative skills. In order to evaluate the system’s user experience and user interaction, the Questionnaire of User Interaction Satisfaction and User Experience Questionnaire were utilised. Forty-four young police officers evaluated the system. Police students showed positive levels of satisfaction with user interaction and overall user experience with minimal negative feedback. Female students showed higher satisfaction with the overall impression compared to male students. Based on the positive feedback regarding the system expansion, the system will be taken into the commercialisation stage in the future to be provided as an essential tool for crime scene education and investigation practices.

Documentation and management of complex 3D morphologies through digital technology

Digital technologies offer an effective solution for documenting and managing complex three-dimensional morphologies, particularly in the context of Underground Built Heritage (UBH). Our research explores novel digitization strategies by employing various technological tools for 3D scanning. We focus on a selected case study: the Grotta di San Michele Arcangelo. Range-based techniques using LiDAR-UAV (Light Detection and Ranging set in Unmanned Aerial Vehicles) were used for the cave’s exterior, integrated with SLAM (Simultaneous Localization and Mapping) technology was employed for the cave’s interior and close-range photogrammetry techniques captured intricate wall decorations. The results yielded precise geometric information crucial for subsequent three-dimensional modelling of architectural and natural spaces. Furthermore, these techniques were integrated to enhance information management, facilitate conservation efforts, and update existing documentation. The resulting comprehensive database is an essential tool for understanding, monitoring, and effectively managing these intricate underground environments.

Speeding up UAV-based crop variability assessment through a data fusion approach using spatial interpolation for site-specific management

Innovations in precision agriculture enhance complex tasks, reduce environmental impact, and increase food production and cost efficiency. One of the main challenges is ensuring rapid information availability for autonomous vehicles and standardizing processes across platforms to maximize interoperability. The lack of drone technology standardisation, communication barriers, high costs, and post-processing requirements sometimes hinder their widespread use in agriculture. This research introduces a standardized data fusion framework for creating real-time spatial variability maps using images from different Unmanned Aerial Vehicles (UAVs) for Site-Specific Crop Management (SSM). Two spatial interpolation methods were used (Inverse Distance Weight, IDW, and Triangulated Irregular Networks, TIN), selected for their computational efficiency and input flexibility. The proposed framework can use different UAV image sources and offers versatility, speed, and efficiency, consuming up to 98 % less time, energy, and computing requirements than standard photogrammetry techniques, providing rapid field information, allowing edge computing incorporation into the UAV data acquisition phase. Experiments conducted in Spain, Serbia, and Finland in 2022 under the H2020 FlexiGroBots project demonstrated a strong correlation between results from this method and those from standard photogrammetry techniques (up to r = 0.93). In addition, the correlation with Sentinel 2 satellite images was as strong as that obtained with photogrammetry-based orthomosaics (up to r = 0.8). The proposed approach could support irrigation leak detection, soil parameter estimation, weed management, and satellite integration for agriculture.

Mobile forensic photogrammetry in the field: Conservative approach to non-collaborative surfaces

Conventional photogrammetry faces challenges with non-textured, transparent, or reflective surfaces, affecting accurate 3D modeling, particularly in forensic documentation. This study evaluates improvements using lower exposure, exposure bracketing, and RAW format for better 3D modeling of non-textured surfaces. Two bodies were photographed under controlled conditions to assess techniques for non-textured surfaces, with a comparison set for textured surfaces. The experiments were conducted in an autopsy room with a Nikon D5500 camera, adjusting for low exposure, exposure bracketing, RAW format, and increased photo redundancy. Models with Meshroom (Alicevision). Our focus was on visual plausibility rather than quantitative metrics. Results indicated that using RAW format with exposure bracketing and low exposure significantly improved 3D models by reducing artificial edges seen with standard JPG images, despite some noise. A redundant series of RAW photos further reduced edge artifacts and noise, demonstrating the effectiveness of averaging photos to enhance model quality. However, these modifications showed marginal improvements on textured surfaces, underscoring their significant benefits primarily for non-textured surfaces. This study highlights the potential of modified photogrammetry techniques in forensic science, particularly for documenting challenging surfaces. It points out the need for further research, given its limitations in sample size and the absence of extensive parameter testing and quantitative analysis.

Mean Texture Depth Estimation of Exposed Aggregate Concrete Pavement Surface Texture Based on Photogrammetry Technique

Mean Texture Depth Estimation of Exposed Aggregate Concrete Pavement Surface Texture Based on Photogrammetry Technique

Measurement Of Spatial-Temporal Waves In The Laboratory Using Computer Vision Technolog

Given the complexity of ocean wave phenomena, physical modeling continues to be essential in various engineering and research applications. Wave height, a characteristic measured in nearly all projects, is commonly assessed using resistance/capacitance wave gauges. However, a significant limitation of these traditional wave gauges is their point-based nature, which restricts their ability to describe detailed spatial wave characteristics, especially during wave nonlinearity or when waveforms change. Furthermore, to reconstruct the 3D wave field, an array of wave gauges is often employed alongside an appropriate interpolation algorithm. Additionally, wave gauges may alter flow/wave conditions in specific scenarios. To address these limitations, various non-intrusive measurement techniques have been proposed. Gomit et al. (2022) classified these into stereoscopic, projection, and light-based methods, including LiDAR (Blenkinsopp et al., 2012), RGB-D cameras (Martínez-Aranda et al., 2018), and binocular stereo vision (Li et al., 2022). However, their applicability is constrained by factors such as optical setup complexity and device cost. With the rapid advancement of computer vision technology, it is possible to improve these issues and better describe temporal and spatial wave field variations using such techniques. This conference contribution details the reconstruction of the temporal and spatial 3D free water surface using photogrammetry techniques applied to images from consumer webcams. It also compares these results with measurements obtained from a wave gauge.

Revisiting the microsurgical anatomy of the sagittal stratum and surgical implications: fiber microdissection and tractography study.

The term "sagittal stratum" was coined by Heinrich Sachs in 1892 to define a parasagittally oriented white matter layer at the temporo-occipital cortex. Although this term has been widely used for more than 100 years, the description, classification, borders, and involved fibers of the structure vary among authors and remain imprecise. Through fiber microdissection and tractography, the authors aimed to define the sagittal stratum and resolve the uncertainty by revealing the relationship of this structure to other cerebral white matter pathways and the orientation of fibers in it. Twenty postmortem human cerebral hemispheres were prepared according to Klingler's method. Fiber dissections were performed under a surgical microscope and with microsurgical techniques. The results of dissection at each step were photographed with 2D and 3D imaging techniques, and 3D photogrammetry techniques were used to create a 360° model. Diffusion tensor imaging and 7T high-resolution MRI were used to confirm the findings. This study revisited the 3D organization of white matter tracts in the sagittal stratum through fiber microdissection and tractography. The microneuroanatomical structure of the sagittal stratum and its special organization with fibers from all three fiber systems are demonstrated. The authors' findings revealed that the sagittal stratum has two layers consisting of four different fiber tracts. Its external layer consists of a long association fiber and a commissural fiber, while its internal layer consists of intertwined projection fibers, including temporo-parieto-occipitopontine fibers and the posterior thalamic peduncle. Detailed microdissection also showed the location of the posterior thalamic peduncle in the most medial site of all posterior hemispheric projection fibers. The structure of the sagittal stratum is distinctive in that it contains all three main fiber systems: association, commissural, and projection. Because of its expansive location in the temporal and occipital lobes, it can be damaged by most neurosurgical pathologies and procedures. The authors emphasize the significance of preserving the sagittal stratum during surgical interventions while also challenging the notion of a "silent" brain, suggesting that the current inability to fully comprehend cerebral function contributes to this misconception. Detailed knowledge of the complex white matter anatomy of the sagittal stratum can guide neurosurgeons in surgical planning and the selection of appropriate surgical approaches with intraoperative orientation for safe surgery and less comorbidity.

A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees

This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs.