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The article concerns the analysis of the dimensional accuracy of point clouds reproducing spatial object made by photographic scanning in the Agisoft Metashape program. Due to the lack of available scientific literature describing the results of research on the accuracy of this type of objects created in the Metashape program, analyses were carried out to determine it. In order to demonstrate the possibilities of wide application of point clouds also in opinion-making practice, three examples of objects with dimensions typical for the area of road accident research were described: a tread trace, a car and a fragment of a road. The obtained point clouds were compared with real objects in terms of dimensional accuracy. It was found that the method is able to provide very good accuracy, with a margin that meets the requirements of typical accident analysis. [ABSTRACT FROM AUTHOR]

The increasing demand for accurate and detailed 3D modeling in fields such as cultural heritage preservation, industrial inspection, and scientific research necessitates advanced techniques to enhance model quality. This paper addresses this necessity by incorporating spectral imaging data to improve the surface detail and reflectivity of 3D models. The methodology integrates spectral imaging with traditional 3D modeling processes, offering a novel approach to capturing fine textures and subtle surface variations. The experimental results of this paper underscore the advantages of incorporating spectral imaging data in the creation of 3D models, particularly in terms of enhancing surface detail and reflectivity. The achieved experimental results demonstrate that 3D models generated with spectral imaging data exhibit significant improvements in surface detail and accuracy, particularly for objects with intricate surface patterns. These findings highlight the potential of spectral imaging in enhancing 3D model quality. This approach offers significant advancements in 3D modeling, contributing to more precise and reliable representations of complex surfaces. [ABSTRACT FROM AUTHOR]

The increasing demand for accurate and detailed 3D modeling in fields such as cultural heritage preservation, industrial inspection, and scientific research necessitates advanced techniques to enhance model quality. This paper addresses this necessity by incorporating spectral imaging data to improve the surface detail and reflectivity of 3D models. The methodology integrates spectral imaging with traditional 3D modeling processes, offering a novel approach to capturing fine textures and subtle surface variations. The experimental results of this paper underscore the advantages of incorporating spectral imaging data in the creation of 3D models, particularly in terms of enhancing surface detail and reflectivity. The achieved experimental results demonstrate that 3D models generated with spectral imaging data exhibit significant improvements in surface detail and accuracy, particularly for objects with intricate surface patterns. These findings highlight the potential of spectral imaging in enhancing 3D model quality. This approach offers significant advancements in 3D modeling, contributing to more precise and reliable representations of complex surfaces.

Abstract Larval settlement and recruitment of sessile organisms are key ecological processes for population recovery and maintenance that occur at scales invisible to the human eye. Accordingly, proxies of recruitment have commonly been quantified using artificial substrata such as settlement tiles made of diverse materials and shapes, which are typically transported to the laboratory for examination. However, it is unknown how much bias is introduced with this sampling strategy and how recruitment quantified on tiles relates to recruitment on nearby natural substrata. Here, we applied techniques that combine macrophotography with photogrammetry (macrophotogrammetry) underwater to monitor benthic communities at submillimetre scale. This application allows the investigation of recruitment and community succession of the earliest life‐history stages in situ and on natural substrata. We tested the use of four different imaging systems, varying in costs from US$ 1400 to US$ 5440. While the most expensive SONY αRiv system provided the best visual output and ground resolution (up to 5 μm/pixel with a + 4 close‐up lens); regardless of systems, 3D models always had a ground resolution ≤23 μm/pixel and errors in planar measurements of submillimetre features were similar among systems. This level of resolution compares well with stereomicroscopy in the range of 5:1 to 10:1 magnification, while providing detailed 3D digital records through time. Using a coral reef example, we apply this approach to demonstrate how it can be used to monitor small reef areas (~300–600 cm2) through time, including the quantification of biophysical metrics such as cover of small facilitative and competitive organisms and microhabitat complexity. We further show that organisms as small 0.5 mm in size, such as 2‐month‐old coral settlers, can be located accurately within the 3D models and measured with a good level of confidence. This method can be readily applied to other benthic environments to elucidate drivers of early recruitment and recovery of benthic organisms following disturbance impacts at very fine scales, directly on natural substrata, to avoid biases inherent with laboratory‐based analyses of artificial surfaces.

The article deals with the use of DJI Phantom 4 RTK unmanned aerial vehicle to obtain morphometric characteristics of the landslide that occurred on May 15 in Tekeli town. A 5-year-old child died as a result of the landslide. During the survey, the site was surveyed using a DJI Phantom 4 RTK quadcopter. On the basis of the obtained aerial photographs, the following were built: a 3D digital elevation model, an orthophoto of the terrain, longitudinal and transverse profiles of the landslide area and the adjacent slope using special software Agisoft Metashape Professional. [ABSTRACT FROM AUTHOR]

The article deals with the experience of using an unmanned aerial vehicle (DJI Matrice 300 RTK) and Agisoft Metashape software to obtain the characteristics of avalanches that descended in late March in the Shukur and Kotyrbulak tracts (Pioneer avalanche) in the Ile Alatau mountains. UAVs can perform observations in remote and dangerous areas, quickly reach the scene, and capture high-quality images and video. In Agisoft Metashape, data processing is performed to create digital elevation maps, 3D models and obtain various geometric data. The resulting data can be used to analyze and predict avalanche processes, as well as to develop measures to reduce the risk of such processes in the future. [ABSTRACT FROM AUTHOR]

The work was performed in the old-growth linden-spruce forest of the Kologrivsky Forest Nature Reserve (Kostroma Oblast, Russia) based on aerial photography with a quadcopter. Automatic detection algorithms made it possible to detect most of the trees in the forest canopy. Tree detection by orthomosaic using neural network algorithm ‘DeepForest’ turned out to be of better quality than detection based on the canopy height model using an algorithm based on the sliding window method. As a rule, both methods showed better results for conifers compared to deciduous trees. Comparison of the average heights of trees estimated from remote data and measured by ground survey did not reveal significant differences. Additional ground surveys to assess the quality of undergrowth detection are needed.

Premise: We recognized the need for a customized imaging protocol for plant specimens at the time of collection for the purpose of three‐dimensional (3D) modeling, as well as the lack of a broadly applicable photogrammetry protocol that encompasses the heterogeneity of plant specimen geometries and the challenges introduced by processes such as wilting. Methods and Results: We developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional. Conclusions: The equipment list and protocols represent a foundation on which additional improvements can be made for specimen geometries outside of the range of the six types considered, and an easy entry into photogrammetry for those who have not previously used it. [ABSTRACT FROM AUTHOR]

Larval settlement and recruitment of sessile organisms are key ecological processes for population recovery and maintenance that occur at scales invisible to the human eye. Accordingly, proxies of recruitment have commonly been quantified using artificial substrata such as settlement tiles made of diverse materials and shapes, which are typically transported to the laboratory for examination. However, it is unknown how much bias is introduced with this sampling strategy and how recruitment quantified on tiles relates to recruitment on nearby natural substrata.Here, we applied techniques that combine macrophotography with photogrammetry (macrophotogrammetry) underwater to monitor benthic communities at submillimetre scale. This application allows the investigation of recruitment and community succession of the earliest life‐history stages in situ and on natural substrata.We tested the use of four different imaging systems, varying in costs from US$ 1400 to US$ 5440. While the most expensive SONY αRiv system provided the best visual output and ground resolution (up to 5 μm/pixel with a + 4 close‐up lens); regardless of systems, 3D models always had a ground resolution ≤23 μm/pixel and errors in planar measurements of submillimetre features were similar among systems. This level of resolution compares well with stereomicroscopy in the range of 5:1 to 10:1 magnification, while providing detailed 3D digital records through time.Using a coral reef example, we apply this approach to demonstrate how it can be used to monitor small reef areas (~300–600 cm2) through time, including the quantification of biophysical metrics such as cover of small facilitative and competitive organisms and microhabitat complexity. We further show that organisms as small 0.5 mm in size, such as 2‐month‐old coral settlers, can be located accurately within the 3D models and measured with a good level of confidence. This method can be readily applied to other benthic environments to elucidate drivers of early recruitment and recovery of benthic organisms following disturbance impacts at very fine scales, directly on natural substrata, to avoid biases inherent with laboratory‐based analyses of artificial surfaces. [ABSTRACT FROM AUTHOR]

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Abstract Premise We recognized the need for a customized imaging protocol for plant specimens at the time of collection for the purpose of three‐dimensional (3D) modeling, as well as the lack of a broadly applicable photogrammetry protocol that encompasses the heterogeneity of plant specimen geometries and the challenges introduced by processes such as wilting. Methods and Results We developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional. Conclusions The equipment list and protocols represent a foundation on which additional improvements can be made for specimen geometries outside of the range of the six types considered, and an easy entry into photogrammetry for those who have not previously used it.

In this paper, we investigated three pine (Pinus sylvestris) forest plots (each of 50 × 50 m), different by age and composition, located in the Prioksko-Terrasny State Nature Biosphere Reserve (Moscow Region, Russia). This study was aimed to evaluate the forest stand attributes based on the photogrammetric point clouds and canopy height models (CHM). For aerial photography, we used the unmanned aerial vehicle (UAV) quadrocopter DJI Phantom 4. At the first step, we used Agisoft Metashape software for the building of dense photogrammetric point clouds and orthophotoplans. Then we used the lidR package in the R environment for processing of dense point clouds. We used a cloth simulation filter for classification of ground points, spatial interpolation algorithm tin for creating a normalised dataset, and the algorithm lmf (local maximum filter) for individual tree detection and tree height assessment. For accuracy assessment, we collected field-based data, and calculated recall (r), precision (p), and F-score (F). Finally, we calculated CHMs (30 cm/pixel) derived from dense point clouds using the pit-free algorithm. To address the value of UAV data for delineating tree crowns, we compared the outputs of CHM data using two common algorithms (watershed and region-growing), and the result of manual orthophotoplans vectorisation. We obtained a high accuracy of individual tree detection. The algorithm found 46.7% to 87.5% of trees accounted on the sample plots by the field-based surveys. The recall (r) value varied from 0.5 to 0.9. The value of p varied from 0.9 to 1.0. The F-score, considering both factors (p and r), varied from 0.7 to 0.9. The highest accuracy was obtained in the site with a single-layer stand, where large trees with well-distinct tree crowns dominated. Spatial heterogeneity of tree stands reduces the accuracy of tree detection. We also found that tree heights estimated on the dense clouds were well matched with tree heights measured in the field. This dependency was described by the linear regression of y = 0.99x, R2 = 0.99. With both the watershed and region-growing algorithms, the total crown area estimation often exceeded the results of orthophotoplans manual vectorisation, where differences reached 25.1%. Differences between two delineation algorithms varied from 0.2% to 19.7% for the same sites. More accurate results were obtained for plots with lesser density of tree stands. Overall, our results have shown the potential of using photogrammetric point clouds for estimating tree attributes (heights and density) in single-layer pine stands. Widely used tree crown segmentation algorithms do not provide reliable estimates of the crown projection area, and more accurate results could be obtained after further improvement of the technique.

RTKLIB which is an open source Global Navigation Satellite Systems (GNSS) software has gained rapid acceptance among Surveying professionals thanks to recent developments in UAS (Unmanned Aerial System) technology. RTKLIB enables standard and precise point positioning (PPP) in real-time and post-processing modes to be performed. As such, UAS users utilize this software to analyze GNSS data collected by GNSS systems on UAS. By being versatile and free, RTKLIB is commonly used by many; however, it is not the only freely available GNSS software. There are also freely available online GNSS data processing software running on servers. These online GNSS data processing services provide data processing in static, kinematic and rapid static modes. Because UAS collect data in kinematic mode, in this study, kinematic data processing by aforementioned software (CSRS-PPP, GAPS and APPS) is analyzed. The results coming from these software are compared against the results produced by photogrammetric software (Agisoft Metashape and Pix4Dmapper). The aim of this practical project is to produce generalizable knowledge about the performance of these software. It is found out that RTKLIB and CSRS-PPP achieved cm-level precision. Yet, GAPS and APPS achieved dm-level precision both for horizontal and vertical coordinates. This study demonstrates the precision and accuracy expected from these software if they are used for kinematic GNSS data processing.

Natural disasters and human interference have endangered heritage structures around the world. Therefore, 3D modeling of buildings is important for historical preservation, particularly in low-income and war-affected countries. The majority of 3D structure surveying acquisition approaches, terrestrial laser scanning (TLS), total station measurements, or traditional photogrammetry require either high-cost technologies or professional user supervision. Structure from motion (SfM) approaches address both of these issues by allowing a non-expert user to produce a dense point cloud for real structures by taking a few 2D photographs with a digital camera and processing them with highly automated and freely available data processing tools. The state of the art for the SfM technique is presented in this paper. Agisoft Metashape, VisualSFM, and Regard3D, three well-known types of SfM software, were examined and compared. The 3D point cloud was scaled and transformed into a local coordinates system using total station instruments that were used to obtain some ground control points (GCPs). Ninety-six 2D digital photographs for the historical Emara Palace in Najran, Saudi Arabia, were obtained as data input, and the image matching, bundle adjustment (BA), completeness, and accuracy of three used packages were calculated and compared. [ABSTRACT FROM AUTHOR]

The fully automated Structure-from-Motion approach for developing digital elevation models and orthomosaics has been known and used in photogrammetry for at least 15 years. Years of practice and experience have allowed researchers to provide a solid description of the applicability and limitations of this method. That being said, the impact of input processing parameters in software on the quality of photogrammetric products has yet to be fully ascertained empirically. This study is aimed at identifying the most advantageous processing workflow to fill this research gap by testing 375 different setup variations in the Agisoft Metashape software for the same set of images acquired using an unmanned aerial vehicle in a proglacial area. The purpose of the experiment was to determine three workflows: (1) the fastest, which has the shortest calculation time; (2) the best quality, which is as accurate as possible, regardless of the time taken for the calculations; and (3) the optimal, which is a compromise between accuracy and calculation time. Each of the 375 processing setup variations was assessed based on final product accuracy, i.e., orthomosaics and digital elevation models. The three workflows were selected based on calculating the height differences between the digital elevation models and the control points that did not participate in their georeferencing. The analyses of the root mean square errors (RMSE) and standard deviations indicate that excluding some of the optimization parameters during the camera optimization stage results in high RMSE and an increase in the values of standard deviation errors. Furthermore, it was shown that increasing the detail of individual processing steps in software does not always positively affect the accuracy of the resulting models. The experiment resulted in the development of three different workflows in the form of Python scripts for Agisoft Metashape software, which will help users to process image sets efficiently in the context of earth surface dynamics studies. [ABSTRACT FROM AUTHOR]

Images of sediment cores are often acquired to preserve primary color information, before such profiles are altered by subsequent sampling and destructive analyses. In many cases, however, no post-processing of these images is undertaken to extract information, despite the fact that image processing can be used to describe and measure structures within the sample. Improvements of RGB (Red/Green/Blue) cameras and image processing algorithms now enable acquisition of high-resolution, metrically calibrated pictures called ortho-images, which have great potential. The way to obtain such ortho-images is by processing several raw images. We propose a semi-automated method that uses metrically calibrated targets to create the ortho-image, using Agisoft Photoscan software and a Python script. The method was tested on sediment cores up to 1.5 m long. It was compared to an approach without markers, one that uses only image matching. The proposed method showed better resolution and less distortion (GSD: 59 µm, RMSE: 7–18 µm). Images acquired without calibrated targets can still be used, by manually positioning points that can then be metrically calibrated. This approach is very useful for smartphone images taken in the field. There are many potential applications for such images of sediment cores, for instance as metric stratigraphic logs to facilitate description of the profile by unit, to study and measure structures (e.g. laminae, instantaneous deposits), or use of image registration or data fusion to create spatial landmarks for non-destructive sensors or destructive laboratory analyses. High-resolution metrically calibrated RGB images of sediment cores are simple to acquire and can play an important role in paleoclimate and paleoenvironmental studies. [ABSTRACT FROM AUTHOR]

The fully automated Structure-from-Motion approach for developing digital elevation models and orthomosaics has been known and used in photogrammetry for at least 15 years. Years of practice and experience have allowed researchers to provide a solid description of the applicability and limitations of this method. That being said, the impact of input processing parameters in software on the quality of photogrammetric products has yet to be fully ascertained empirically. This study is aimed at identifying the most advantageous processing workflow to fill this research gap by testing 375 different setup variations in the Agisoft Metashape software for the same set of images acquired using an unmanned aerial vehicle in a proglacial area. The purpose of the experiment was to determine three workflows: (1) the fastest, which has the shortest calculation time; (2) the best quality, which is as accurate as possible, regardless of the time taken for the calculations; and (3) the optimal, which is a compromise between accuracy and calculation time. Each of the 375 processing setup variations was assessed based on final product accuracy, i.e., orthomosaics and digital elevation models. The three workflows were selected based on calculating the height differences between the digital elevation models and the control points that did not participate in their georeferencing. The analyses of the root mean square errors (RMSE) and standard deviations indicate that excluding some of the optimization parameters during the camera optimization stage results in high RMSE and an increase in the values of standard deviation errors. Furthermore, it was shown that increasing the detail of individual processing steps in software does not always positively affect the accuracy of the resulting models. The experiment resulted in the development of three different workflows in the form of Python scripts for Agisoft Metashape software, which will help users to process image sets efficiently in the context of earth surface dynamics studies.

Coral growth rates vary significantly with environmental conditions and are thus important indicators of coral health and reef carbonate production. Despite the importance of this metric, data are sparse for most coral genera and species globally, including for many key reef‐building species. Traditional methods to obtain growth rates, such as coral coring or staining with Alizarin are destructive and only work for a limited number of species and morphological growth forms.Emerging approaches, using underwater photogrammetry to create digital models of coral colonies, are providing novel and non‐invasive ways to explore colony‐scale growth patterns and to address existing knowledge gaps. We developed an easy‐to‐follow workflow to construct three‐dimensional (3D) models from overlapping photographs and to measure linear, radial and vertical extension rates of branching, massive and encrusting corals after aligning colony models from subsequent years.The method presented here was applied to measure extension rates for 46 colonies of nine coral species in the remote Chagos Archipelago, Indian Ocean. Proposed image acquisition and software settings produced 3D models of consistently high resolution and detail (precision ≤ 0.2 mm) and variability in growth measurements was small despite manual alignment, clipping and ruler placement (SD ≤ 0.9 mm). Measured extension rates for the Chagos Archipelago are similar to published rates in the Indo‐Pacific where comparable data are available, and provide the first published rates for several species. For encrusting corals, the results emphasize the importance of differentiating between radial and vertical growth.Photogrammetry and 3D model comparisons provide a fast, easy, inexpensive and non‐invasive method to quantify coral growth rates for a range of species and morphological growth forms. The simplicity of the presented workflow encourages its repeatability and permits non‐specialists to learn photogrammetry with the goal of obtaining linear coral growth rates. Coral growth rates are essential metrics to quantify functional consequences of ongoing community changes on coral reefs and expanded datasets for key coral taxa will aid predictions of geographic variations in coral reef response to increasing global stressors. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include (1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and (2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.

DSM generation from satellite imagery is a long-lasting issue and it has been addressed in several ways over the years; however, expert and users are continuously searching for simpler but accurate and reliable software solutions. One of the latest ones is provided by the commercial software Agisoft Metashape (since version 1.6), previously known as Photoscan, which joins other already available open-source and commercial software tools. The present work aims to quantify the potential of the new Agisoft Metashape satellite processing module, considering that to the best knowledge of the authors, only two papers have been published, but none considering cross-sensor imagery. Here we investigated two different case studies to evaluate the accuracy of the generated DSMs. The first dataset consists of a triplet of Pléiades images acquired over the area of Trento and the Adige valley (Northern Italy), which is characterized by a great variety in terms of geomorphology, land uses and land covers. The second consists of a triplet composed of a WorldView-3 stereo pair and a GeoEye-1 image, acquired over the city of Matera (Southern Italy), one of the oldest settlements in the world, with the worldwide famous area of Sassi and a very rugged morphology in the surroundings. First, we carried out the accuracy assessment using the RPCs supplied by the satellite companies as part of the image metadata. Then, we refined the RPCs with an original independent terrain technique able to supply a new set of RPCs, using a set of GCPs adequately distributed across the regions of interest. The DSMs were generated both in a stereo and multi-view (triplet) configuration. We assessed the accuracy and completeness of these DSMs through a comparison with proper references, i.e., DSMs obtained through LiDAR technology. The impact of the RPC refinement on the DSM accuracy is high, ranging from 20 to 40% in terms of LE90. After the RPC refinement, we achieved an average overall LE90

This study proposes a new measurement system based on Structure from Motion (SfM) photogrammetry to evaluate tunnel faces. The main goal is to determine the tunnel face discontinuity patterns and orientations for the tunnel face stability evaluation. A 3D point cloud model is generated from overlapping images of the tunnel face 3D polystyrene model through SfM photogrammetry. The discontinuity patterns and orientations are determined through facet extraction of the K-D tree plugin in CloudCompare. The same sets of overlapping images were analyzed in four different quality settings (low, medium, high, and ultra-high) with various sets of point cloud number that control the accuracy of the discontinuity's measurement. In this study, the high-quality setting shows the consistent measurement of the discontinuity patterns and orientations compared with the real 3D polystyrene tunnel face model. Validation of the pattern results from CloudCompare was carried out using JudGeo. Cluster analysis shows two sets of discontinuity with means of dip and dip direction of 82°/164° and 84°/307°, respectively. Both orientations were manually verified with a geological compass. The high-quality point cloud and manual compass measurement of the orientation highlight the similarity of the concentration for both discontinuity sets for the replica tunnel face. This tunnel face proposed method has the advantages of evaluating inaccessible areas and avoiding professional bias. • To analyze discontinuity using 2D and 3D image analysis techniques by replicating a 3D tunnel face model made of polystyrene. • Identify discontinuity pattern and orientation for tunnel face stability evaluation. • Development of a new quantitative discontinuity estimation towards tunnel face evaluation using image analysis technique. [ABSTRACT FROM AUTHOR]

• To analyses quantitative results of discontinuity using image analysis technique. • Rock mass discontinuity detection using crack pattern analysis of tunnel face. • Development of a new quantitative discontinuity estimation towards the automation of tunnel face evaluation. • Verification of the developed assessment system of the tunnel face using the JH Rock Mass Classification system. Crack is a common condition that affects water-rich loess tunnels, and it negatively influences the reliability and safety of the tunnel. Jointed rock masses are commonly found during underground excavation. Many underground openings have failed during excavation and operation. The evaluation of tunnel faces by using a new measurement system based on structure from motion (SfM) photogrammetry is proposed in this study. The main objective is to determine the discontinuity pattern and orientation of tunnel faces as an input for tunnel face stability evaluation. A set of overlapping images obtained from a tunnel face replica using 3D polystyrene and the SfM photogrammetry approach is utilized to generate a 3D point cloud model. The discontinuity pattern and orientation are determined via facet extraction of the K D -tree plugin in CloudCompare. The same set of overlapping images is analyzed in four different quality settings (low, medium, high, and ultrahigh) with different sets of point cloud numbers that control the accuracy of the measurement of discontinuity. Results show that the high-quality setting presents a consistent measurement of the discontinuity pattern and orientation in contrast to the real 3D polystyrene tunnel face model. 2D pattern results from CloudCompare are validated using the fractal analysis method in an image analysis software, where the image is converted into a binary-one. Two sets of discontinuity are derived. The means of dip and dip direction from the cluster analysis are 82°/164° and 84°/307°, respectively. The orientations are verified through manual compass measurement. The orientations for manual compass measurement for Sets 1 and 2 are 86°/151° and 80°/303°, respectively. The discontinuities obtained from the high-quality setting of the point cloud and manual measurement of the orientation by using a geological compass highlight the similarity of the discontinuity plane in both discontinuity sets for the tunnel face replica. The proposed method for tunnel face evaluation has good judgment for tunnel support and the prevention of biased analyses by professionals. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include (1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and (2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications. [ABSTRACT FROM AUTHOR]