Distribution Of Control Points Research Articles

Evaluation of 2D affine — hand-crafted detectors for feature-based TLS point cloud registration

Abstract The development of modern surveying methods, particularly, Terrestrial Laser Scanning (TLS), has found wide application in protecting and monitoring engineering and objects and sites of cultural heritage. For this reason, it is crucial that several factors a˛ecting the correctness of point cloud registration are considered, including the correctness of the distribution of control points (both signalised and natural), the quality of the process, and robustness analysis. The aim of this article is to evaluate the quality and correctness of TLS registration based on point clouds converted to raster form (in spherical mapping) and hand-crafted detectors. The expanded Structure-from-Motion (SfM) was used to detect the tie points for TLS registration and reliability assessment. The results demonstrated that affine detectors are useful in detecting a high number of key points (increased for point detectors by 8–12 times and for blob detectors by about 10–24 times), improving the quality and TLS registration completeness. For the registration accuracy of point cloud on signalised check points, the lower values can be noted for maximum RMSE errors for blob affine detectors than detectors and larger values for corner detectors and affine detectors (not more than 4 mm in the extreme cases, typically 2 mm). The commonly-applied target-based registration method yields similar results (di˛erences do not exceed – in extreme cases – 3.5 mm, typically less than 2 mm), proving that using affine detectors in the TLS registration process is and reasonable and can be recommended.

The Importance of Ground Control Points In A Photogrammetric Workflow

Abstract This study investigates the optimal distribution and pattern of ground control points (GCPs) in aerial photogrammetric projects. Aerial triangulation (AT), also known as bundle adjustment, is the fundamental step in refining 3D reconstruction models and camera positions, thereby minimizing reprojection errors. The study utilizes data from a national project in Romania, employing high-resolution aerial images acquisition using photogrammetric sensors. The project has rigorous requirements of ground control points (GCP) placement and field measurements using GNSS and geometric leveling techniques. The study employs various scenarios, manipulating the number and distribution of GCPs, to assess their influence on planimetric and altimetric accuracy. Results indicate that the configuration and number of GCPs significantly affect the accuracy of photogrammetric products, such as dense image point clouds, digital surface models, and orthophotos. Moreover, the study underscores the importance of precise GCP determination methods, especially in regions lacking a precise gravimetric geoid model. In scenarios with inadequate GCP coverage the outcomes have inferior quality, emphasizing the critical role of GCPs in ensuring the quality of photogrammetric products. Overall, the research gives a clear view on the best placement patterns of GCPs and their influence on AT process evaluation performed in check points (CHKs).

GLS‐PIA: n‐Dimensional Spherical B‐Spline Curve Fitting based on Geodesic Least Square with Adaptive Knot Placement

AbstractDue to the widespread applications of curves on n‐dimensional spheres, fitting curves on n‐dimensional spheres has received increasing attention in recent years. However, due to the non‐Euclidean nature of spheres, curve fitting methods on n‐dimensional spheres often struggle to balance fitting accuracy and curve fairness. In this paper, we propose a new fitting framework, GLS‐PIA, for parameterized point sets on n‐dimensional spheres to address the challenge. Meanwhile, we provide the proof of the method. Firstly, we propose a progressive iterative approximation method based on geodesic least squares which can directly optimize the geodesic least squares loss on the n‐sphere, improving the accuracy of the fitting. Additionally, we use an error allocation method based on contribution coefficients to ensure the fairness of the fitting curve. Secondly, we propose an adaptive knot placement method based on geodesic difference to estimate a more reasonable distribution of control points in the parameter domain, placing more control points in areas with greater detail. This enables B‐spline curves to capture more details with a limited number of control points. Experimental results demonstrate that our framework achieves outstanding performance, especially in handling imbalanced data points. (In this paper, “sphere” refers to n‐sphere (n ≥ 2) unless otherwise specified.)

Beach surface model construction: A strategy approach with structure from motion - multi-view stereo

In contrast to traditional beach profiling methods like topographic surveys and GNSS, which pose significant challenges in terms of cost and time, this research underscores the efficiency, cost-effectiveness, and simplicity of terrestrial photogrammetry employing the Structure from Motion-Multi View Stereo (SfM-MVS) method. Notably, this approach enables the utilization of commonplace devices such as smartphones for data capture. The methodology integrates a 12-megapixel camera for image acquisition, processed through Agisoft Metashape Professional software, and validated for accuracy using ground control points (GCPs) and checkpoints (CKPs) calibrated via GNSS. Findings reveal substantial disparities in positional accuracy according to the Ground Control Points distribution. The study underscores the critical role of strategically distributing GCPs and CKPs in effectively mapping coastal areas, thus affirming the potential of SfM-MVS as a powerful and accessible tool for coastal monitoring initiatives. This research contributes significantly to advancing the efficiency and accessibility of beach profile monitoring, offering invaluable insights for researchers and practitioners in coastal management and environmental conservation efforts.•A simplified beach profile modeling methodology is proposed.•The method is faster and more cost-effective than traditional surveys (RTK GNSS, lidar, RPA).•The study highlights the importance of GCP and CKP distribution in enhancing SfM-MVS accuracy for coastal mapping.

A modified U-Net convolutional neural network for segmenting periprostatic adipose tissue based on contour feature learning

ObjectiveThis study trains a U-shaped fully convolutional neural network (U-Net) model based on peripheral contour measures to achieve rapid, accurate, automated identification and segmentation of periprostatic adipose tissue (PPAT). MethodsCurrently, no studies are using deep learning methods to discriminate and segment periprostatic adipose tissue. This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. This paper adopts a supervised learning method on the labeled dataset, combining the probability and spatial distribution of control points, and proposes a weighted loss function to optimize the neural network's convergence speed and detection performance. Based on high-precision detection of control points, this paper uses a convex curve fitting to obtain the final PPAT contour. The imaging segmentation results were compared with those of a fully convolutional network (FCN), U-Net, and semantic segmentation convolutional network (SegNet) on three evaluation metrics: Dice similarity coefficient (DSC), Hausdorff distance (HD), and intersection over union ratio (IoU). ResultsCropped images with a 270 × 270-pixel matrix had DSC, HD, and IoU values of 70.1%, 27 mm, and 56.1%, respectively; downscaled images with a 256 × 256-pixel matrix had 68.7%, 26.7 mm, and 54.1%. A U-Net network based on peripheral contour characteristics predicted the complete periprostatic adipose tissue contours on T2W images at different levels. FCN, U-Net, and SegNet could not completely predict them. ConclusionThis U-Net convolutional neural network based on peripheral contour features can identify and segment periprostatic adipose tissue quite well. Cropped images with a 270 × 270-pixel matrix are more appropriate for use with the U-Net convolutional neural network based on contour features; reducing the resolution of the original image will lower the accuracy of the U-Net convolutional neural network. FCN and SegNet are not appropriate for identifying PPAT on T2 sequence MR images. Our method can automatically segment PPAT rapidly and accurately, laying a foundation for PPAT image analysis.

Unmanned Aerial Vehicle Based Power Pole Localization Using Image Stitching

When facing natural disasters, power transmission and distribution towers are prone to tilting, collapsing, and other situations, which can easily lead to line faults and subsequently cause power outage incidents. In order to quickly and accurately locate abnormal towers and restore power supply promptly, the power maintenance department typically employs unmanned aerial vehicles (UAVs) for post-disaster inspections of power line towers. Traditional methods for UAV ground target localization mainly rely on the UAV’s own position, attitude, and onboard sensor information to indirectly calculate the target’s position. This method is influenced by the Global Positioning System (GPS) and Inertial Navigation System (INS), relying on high-precision sensors. To achieve precise positioning of power line towers with simplified equipment on UAVs, this paper proposes a visual passive localization method based on ground control points. Addressing the sparse distribution of ground control points, a solution based on image stitching is introduced to overcome difficulties in localization caused by insufficient ground control points.

Influence of quantity, quality, horizontal and vertical distribution of ground control points on the positional accuracy of UAV survey

Influence of quantity, quality, horizontal and vertical distribution of ground control points on the positional accuracy of UAV survey

Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation.

Cardiovascular diseases (CVDs) are the leading cause of death, affecting the cardiac dynamics over the cardiac cycle. Estimation of cardiac motion plays an essential role in many medical clinical tasks. This article proposes a probabilistic framework for image registration using compact support radial basis functions (CSRBFs) to estimate cardiac motion. A variational inference-based generative model with convolutional neural networks (CNNs) is proposed to learn the probabilistic coefficients of CSRBFs used in image deformation. We designed two networks to estimate the deformation coefficients of CSRBFs: the first one solves the spatial transformation using given control points, and the second one models the transformation using drifting control points. The given-point-based network estimates the probabilistic coefficients of control points. In contrast, the drifting-point-based model predicts the probabilistic coefficients and spatial distribution of control points simultaneously. To regularize these coefficients, we derive the bending energy (BE) in the variational bound by defining the covariance of coefficients. The proposed framework has been evaluated on the cardiac motion estimation and the calculation of the myocardial strain. In the experiments, 1409 slice pairs of end-diastolic (ED) and end-systolic (ES) phase in 4-D cardiac magnetic resonance (MR) images selected from three public datasets are employed to evaluate our networks. The experimental results show that our framework outperforms the state-of-the-art registration methods concerning the deformation smoothness and registration accuracy.

Improvement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient's dose distribution.

Deep learning-based virtual patient-specific quality assurance (QA) is a novel technique that enables patient QA without measurement. However, this method could be improved by further evaluating the optimal data to be used as input. Therefore, a deep learning-based model that uses multileaf collimator (MLC) information per control point and dose distribution in patient's CT as inputs was developed. Overall, 96 volumetric-modulated arc therapy plans generated for prostate cancer treatment were used. We developed a model (Model 1) that can predict measurement-based gamma passing rate (GPR) for a treatment plan using data stored as a map reflecting the MLC leaf position at each control point (MLPM) and data of the dose distribution in patient's CT as inputs. The evaluation of the model was based on the mean absolute error (MAE) and Pearson's correlation coefficient (r) between the measured and predicted GPR. For comparison, we also analyzed models trained with the dose distribution in patient's CT alone (Model 2) and with dose distributions recalculated on a virtual phantom CT (Model 3). At the 2%/2mm criterion, MAE[%] and r for Model 1, Model 2, and Model 3 were 2.32%±0.43% and 0.54±0.03, 2.70%±0.26%, and 0.32±0.08, and 2.96%±0.23% and 0.24±0.22, respectively; at the 3%/3mm criterion, these values were 1.25%±0.05% and 0.36±0.18, 1.57%±0.35% and 0.19±0.20, and 1.39%±0.32% and 0.17±0.22, respectively. This result showed that Model 1 exhibited the lowest MAE and highest r at both criteria of 2%/2mm and 3%3mm. These findings showed that a model that combines the MLPM and dose distribution in patient's CT exhibited a better GPR prediction performance compared with the other two studied models.

Using existing infrastructure as ground control points to support citizen science coastal UAS monitoring programs

Recent publications have described the ability of citizen scientists to conduct unoccupied aerial system (UAS) flights to collect data for coastal management. Ground control points (GCPs) can be collected to georeference these data, however collecting ground control points require expensive surveying equipment not accessible to citizen scientists. Instead, existing infrastructure can be used as naturally occurring ground control points (NGCPs), although availably of naturally occurring ground control point placement on such infrastructure differs from published best practices of ground control point placement. This study therefore evaluates the achievable accuracy of sites georeferenced with naturally occurring ground control points through an analysis of 20 diverse coastal sites. At most sites naturally occurring ground control points produced horizontal and vertical root mean square errors (RMSE) less than 0.060 m which are similar to those obtained using traditional ground control points. To support future unoccupied aerial system citizen science coastal monitoring programs, an assessment to determine the optimal naturally occurring ground control point quantity and distribution was conducted for six coastal sites. Results revealed that generally at least seven naturally occurring ground control points collected in the broadest distribution across the site will result in a horizontal and vertical root mean square errors less than 0.030 m and 0.075 m respectively. However, the relationship between these placement characteristics and root mean square errors was poor, indicating that georeferencing accuracy using naturally occurring ground control points cannot be optimized solely through ideal quantity and distribution. The results of these studies highlight the value of naturally occurring ground control points to support unoccupied aerial system citizen science coastal monitoring programs, however they also indicate a need for an initial accuracy assessment of sites surveyed with naturally occurring ground control points at the onset of such programs.

Improvement of FFD parametric approach in the application of a lifting body

AbstractFFD (free-form deformation method) is one of the most commonly used parameterisation methods at present. It places the parameterised objects inside the control volume through coordinate system transformation, and controls the control volume through control points, thus realising the deformation control of its internal objects. Firstly, this paper systematically analyses and compares the characteristics and technical requirements of Bernstein, B-spline and NURBS (non-uniform rational b-splines) basic functions that can be adopted by FFD, and uses the minimum number of control points required to achieve the specified control effect threshold to express the control capability. Aiming at the problem of discontinuity at the right end in the actual calculation of B-spline basis function, a method of adding a small epsilon is proposed to solve it. Then, three basic functions are applied to the FFD parameterisation method, respectively, and the differences are compared from two aspects of the accurate expression of the model and the ability of deformation control. It is found that the BFFD (b-spline free-form deformation) approach owns better comprehensive performance when the control points are distributed correctly. In this paper, the BFFD method is improved, and a p-BFFD (reverse solution points based BFFD) method based on inverse solution is proposed to realise the free distribution of control points under the specified topology. Further, for the lifting body configuration, the control points of the p-BFFD method are brought closer to the airframe forming the EDGE-p-BFFD (edge constraints based p-BFFD) method. For the case in this paper, the proposed EDGE-p-BFFD method not only has fairly high parameterisation accuracy, but also reduces the expression error from 1.01E-3 to 1.25E-4, which is nearly ten times. It can also achieve effective lifting body guideline constraints, and has the ability of local deformation adapting to the configuration characteristics. In terms of the proportion of effective control points, the EDGE-p-BFFD method increases the proportion of effective control points from 36.7% to 50%, and the more control points, the more obvious the proportion increase effect. The new method also has better effect on the continuity of geometric deformation. At the same time, this paper introduces the independent deformation method of the upper and lower surfaces based on the double control body frames, which effectively avoids the deformation coupling problem of the simultaneous change of the upper and lower surfaces caused by the movement of control points in the traditional single control framework.

UTILIZATION OF UNMANNED AERIAL VEHICLE (UAV) FOR MEASUREMENT OF SURFACE COAL MINING SITUATION

The rapid development of technology has given rise to innovations and new ways of solving problems. One of them is measuring the situation of surface coal mines. So far, measurements have been made using a Laser Scanner. However, now the Unmanned Aerial Vehicle (UAV) can replace it by considering the aspects of work safety, effectiveness, and efficiency in the field. This study analyzes the influence of the distribution of Ground Control Points (GCP). It compares the measurement method using a Laser Scanner with a UAV in a surface coal mine situation. In addition, this study aims to determine the exact parameters regarding the distribution and number of control points in using UAVs to measure surface coal mining situations. The results showed that the UAV could be used to measure the situation of surface coal with the results of the acquisition parameter research following the 1:1000 PERBIG scale tolerance map, with a tolerance value of <10 cm and the volume difference between the Laser Scanner and UAV is 0.131%. Regarding time analysis and work safety, measurements using UAVs can measure surface mining situations for ± 40 minutes. In addition, personnel do not need to approach the measurement object, so it is better than measurements using a Laser Scanner.

A Method to Construct Depth Datum Geodesic Height Model for GNSS Bathymetric Survey

Water depth measurement requires the establishment of one or more tidal stations in the survey area for synchronous water level observation, and finally the water depth is estimated to the depth datum. The non-tidal observation measuring has high efficiency and avoids the water level correction error caused by tidal observation in traditional sounding. Therefore, non-tidal observation measuring has become an effective water depth measurement method in offshore and inland water. However, datum conversion in non-tide operation is mostly based on the polynomial fitting method. The accuracy of this method is influenced by the distribution of datum control points, topographic relief and operation ranges. In this paper, we present a method to construct a depth datum geodesic height model, which can directly obtain a bathymetric database of depth data in a GNSS bathymetric survey. The model incorporates the continuous depth datum and the mean sea level of geodetic height in the same area. Through the numerical simulation of tidal wave motion in regional water, the tidal model is obtained. Based on the grid model, the tidal level is extracted from the tidal model for harmonic analysis, and a continuous depth datum model is established. Mean sea level geodetic height is from the CNES-CLS2015 Average Sea Surface Model. In this paper, the model is confirmed in the South Yellow Sea area. The results show that the accuracy of the depth datum model, and the depth datum geodetic height model meets the accuracy requirements of the datum.

Analyzing spatial and temporal evolution of groundwater salinization through Multivariate Statistical Analysis and Hydrogeochemical Facies Evolution-Diagram

The growing groundwater withdrawal rates in coastal aquifers in arid/semi-arid regions exacerbate seawater intrusion and saltwater upconing by causing groundwater salinization and potential adverse and cascading effects to related groundwater-depending systems. This study aims to highlight the dynamics of groundwater salinization in time and space by comparing the efficacy of statistical (hierarchical cluster and factor analyses) and hydrogeochemical (hydrogeochemical facies evolution) methods. Multi-temporal groundwater samples collected from the monitoring well network in the study area (Salento Aquifer, Puglia region, Southern Italy) have been considered to recognize such dynamics. By comparing the spatial and temporal evolution of water clusters, factor scores, and hydrogeochemical facies, the proposed methodological approach enables the identification of zones characterized by low dynamics of freshening and intrusion processes (with invariant features during the investigated period), which correspond to groundwater recharge areas and zones subject to groundwater salinization respectively. On the contrary, a high spatial and temporal variability of salinization dynamics typifies the zones subject to alternation of groundwater characteristics. These results allow outlining a preliminary hazard map related to groundwater salinization processes, which might be a useful tool for policymakers and stakeholders involved in groundwater management of coastal aquifers. Results suggest that generally, a thoughtful understanding of limitations concerning the aquifer heterogeneity and anisotropy, distribution and density of control points, and depth of sampling is crucial for handling the study outcomes, especially for the aims of management.

Effect of survey parameters on unmanned aerial vehicles-derived topography for coastal dune monitoring

Coastal dunes are fragile ecosystems emerging at the interface between marine and continental environments. They provide multiple services, among which are the protection against the impact of storms and the hosting of diverse and unique species of fauna and flora. However, changes in the topography or biological component of these systems may endanger the perpetuation of service provision. Topographic changes within dunes can significantly differ in magnitude depending on the type of process (i.e., marine or aeolian) and the temporal scale of analysis (event to annual scale), making their monitoring a challenging task. In recent years, unmanned aerial vehicles (UAVs) have been increasingly used to monitor coastal dunes, proving to be a cost-efficient methodology for the collection of topographic data. Yet, the application of UAVs in combination with the structure from motion approach to obtain digital surface models (DSMs) presents some limitations related to the level of accuracy provided for the evaluation of topographical changes in dunes with low sedimentation rates. This work explores different survey configurations using UAVs flying at low altitudes with the aim of obtaining high-quality DSMs with vertical accuracies preferably around or lower than 0.04 m. Several tests were performed to evaluate the influence of different parameters on the accuracy of the DSM, including flight altitude and orientation, density and spatial distribution of ground control points (GCPs), terrain slope, vegetation cover, and sun-related parameters. The results indicate that the intended accuracies can be obtained by combining overlapped perpendicular flights, GCPs distributed regularly following a diamond grid, with densities of at least 6 GCPs per hectare, sun altitudes between 30 and 40 deg, and a total solar radiation per hour between 1750 and 2250 KJ / m2. In addition, better results were obtained across gentle slope areas, suggesting the eventual need to adapt to the particularities of each site to ensure the accuracy.

ASSESSING THE ACCURACY OF GEOREFERENCED POINT CLOUDS FROM UAS IMAGERY

Abstract. Unmanned Aircraft System (UAS) mapping methods determine the three-dimensional (3D) position of surface features. UAS mapping is often used, compared to traditional mapping techniques, such as the total station (TS), Global Navigation Satellite System (GNSS) and terrestrial laser scanning (TLS). Traditional mapping methods have become less favourable due to efficiency and cost, especially for medium to large areas. As UAS mapping increases in popularity, the need to verify its accuracy for topographic mapping is evident. In this study, an assessment of the accuracy of UAS mapping is performed. Our results suggest that there are many factors that affect the accuracy of UAS photogrammetry products. In specific, the distribution and density of ground control points (GCPs) are particularly significant for a study area of 2.861 km2 in size. The best results were obtained by strategizing the distribution and density of GCPs; where, the root mean square error (RMSE) for the X, Y, Z, and 3D was minimized to 0.012, 0.021, 0.038, and 0.045 meters, respectively, by applying a total of 15 GCPs in the aerotriangulation. Therefore, it may be concluded that UAS photogrammetric mapping can meet sub-decimeter accuracy for topographic mapping, if proper planning, data collection and processing procedures are followed.

Co-registration of multi-sensor UAV imagery. Case study: Boreal forest areas

ABSTRACT Monitoring the regeneration process of a forest is an important part of forestry management. Compared to traditional methods of counting tree species, UAVs have been a revolutionary means of saving time and costs due to the temporal and spatial flexibility of data collection. In turn, the integration of multispectral cameras allows the traditional vegetation indices that have been used with satellite imagery to be obtained. However, data from multispectral cameras must be combined with data from other types of sensors, such as RGB. It is therefore necessary to co-register all the information in order to obtain combined vegetation indices and carry out segmentation processes that allow the identification of the different tree species. In this study, the coordinate transformation methods available in QGIS software through the georeferencer plugin are evaluated. It also studies the influence of the number and distribution of control points on the accuracy of the transformation. It is concluded that of the transformation methods studied, TPS transformation has the highest accuracy with an MAE of 0.9 pixels and a deviation of 0.6 pixels, providing a minimum of 10 control points and a stratified or edge distribution.

A Comparative Analysis of Unmanned Aircraft Systems in Low Altitude Photogrammetric Surveys

Comparing photogrammetric performances of four user-grade unmanned aircraft systems (UAS) is the main aim of this paper. This study investigates what is the more suitable UAS for specific applications considering the required scale factor, such as for architectural, environmental and restoration purposes. Some photogrammetric surveys were conducted in a 5 ha area using a Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2 and Mavic Mini 2. These unmanned aircrafts are commercial systems used mainly by private professionals. Some photogrammetric reconstructions were carried out by varying flight altitude and camera settings of the 4 UAS. Structure-from-motion (SfM) algorithms were applied to the images taken from the UASs. The surveys’ quality was analyzed by comparing the ground targets’ coordinates measured on the field with indirect georeferencing through global navigation satellite system (GNSS). Fifty targets were installed and arranged following a kind of regular grid. For each photogrammetric flight, the boundary conditions were maintained the same, as well as the flight trajectories and the ground control point distribution. Altimetric and planimetric residuals were reported and compared for each photogrammetric survey. Using a regular grid of ground targets, the result obtained from Phantom 4 is one order of magnitude better than the ones obtained from the other UASs. Mavic Mini 2 leads to an error average of about 5 cm. Remembering that the Mavic Mini 2 is an ultralight drone (it does not require a pilot’s license), it could significantly reduce costs compared to all the others.

Performing low-altitude photogrammetric surveys, a comparative analysis of user-grade unmanned aircraft systems

This paper shows results of comparing performances of four unmanned aircraft systems (UAS) in terms of photogrammetric survey’s quality. This study aims to investigate what is the more suitable UAS for specific applications considering the required scale factor, such as for architectural, environmental, and restoration purposes. A series of photogrammetric surveys were conducted in a hilly area of about 5 ha using Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2, and Mavic Mini 2. These unmanned aircrafts are commercial user–grade systems used mainly by private professionals. Several photogrammetric reconstructions were performed by varying essential parameters, such as flight altitude and cameras of remotely piloted aircraft systems (RPAS), applying structure-from-motion (SfM) algorithms to the images taken from the UAS. The surveys’ quality was analyzed by comparing the ground targets’ coordinates extrapolated from the point clouds to those measured on the field with indirect georeferencing through GNSS technology. Fifty targets were installed and arranged following a reasonably regular mesh. The boundary conditions were maintained the same for each flight mission, flight trajectories, and the ground control point distribution on the ground. For each survey made by each of the four UAS, altimetric and planimetric residuals were reported and compared. Average residuals from Phantom 4 Adv, about 15 mm, almost disappear compared to the other UASs; the discrepancy is one order of magnitude. With a regular grid geometry of ground targets, the Mavic Mini 2 led to an error average of about 5 cm. Remembering that the Mavic Mini 2 is an ultralight drone (does not require a pilot's license), it could significantly reduce cost compared to the other systems.

The effect of the distribution and numbers of ground control points on the precision of producing orthophoto maps with an unmanned aerial vehicle

ABSTRACT Ground control points (GCPs) are used in the process of indirectly georeferencing unmanned aerial systems (UAS) images. The aim of this study was to determined how many GCPs and what kind of distribution is required in order to obtain high precision results. In the study, three scenarios were investigated. The GCPs were tested in edge distribution in the first scenario, in the middle distribution in the second scenario, and in homogeneous distribution in the third scenario. In the first scenario, the edge distribution was supported by the central GCPs, while in the second scenario, the central GCPs were supported by the edge GCPs. Each scenario was initiated with four GCPs. The number of GCPs was increased by four, resulting in the utilization of a total of 35 GCPs. In addition, 30 check points (CHP) were used. The number of orthophoto and DSM produced in the study was 33. In the study, the best planimetric accuracy was obtained with the first scenario, namely with the edge distribution (RMSE xy: 0.033 m). Altimetric accuracy was obtained with the third scenario, namely with homogeneous distribution (RMSE z: 0.048 m).