Number Of Ground Control Points Research Articles

Концепция и некоторые результаты разработки инструмента оперативной оценки изменений рельефа на оползнеопасных, склонных к обрушению и пляжных участках берега

The paper describes the concept of building a specialized geoinformation tool designed for automated execution of a number of operations related to the implementation of calculations to assess changes in the relief of individual sections of the coastal zone of the sea. The tool is used for monitoring areas with low stability, prone to landslide and landslide processes, as well as beaches with a quantitative assessment of their spatially distributed volume changes. The initial data for calculations are arrays generated by the program for processing aerial photographs obtained with drones in photogrammetry mode. The algorithmic basis of the tool is the well-known geoinformation tool named Cut & Fill, used in conjunction with a database on the location and geometry of a large number of sections of the western coast of Crimea. The sequence of operations involves sampling from two or more arrays of points according to a given spatial location, performing vertical alignment with use the number of ground control points, performing interpolation into a regular grid of the size accepted for each section, calculations and presenting the results in tabular and graphical form. All the results obtained are saved for reuse and, in addition, their graphical analysis for spatial and vertical alignment is possible. It is also possible to build vertical sections on any row or column of a regular grid. Practical testing of the instrument was performed for three surveys of a landslide-prone area in the area of Tolstoy Cape a year before the catastrophic landslide on 18.01.2019, immediately after the event and in 2021 as a result of monitoring program. The operational assessment showed that all operations for performing calculations on two grids were performed within one hour after the survey with detailed and reliable results.

Pursuing 3-D Scene Structures With Optical Satellite Images From Affine Reconstruction to Euclidean Reconstruction

How to use multiple optical satellite images to recover the 3D scene structure is a challenging and important problem in the remote sensing field. Most existing methods in literature have been explored based on the classical RPC (Rational Polynomial Coefficients) camera model which requires at least 39 GCPs (ground control points), however, it is non-trivial to obtain such a large number of GCPs in many real scenes. Addressing this problem, we propose a hierarchical reconstruction framework based on multiple optical satellite images, which needs only 4 GCPs to fully-automated reconstruct the 3D scene structure. The proposed framework is independent from the RPC model and composed of a dense affine reconstruction stage and a followed affine-to-Euclidean upgrading stage: At the dense affine reconstruction stage, a dense affine reconstruction approach is explored for pursuing the 3D affine scene structure without any GCP from input satellite images. Then at the affine-to-Euclidean upgrading stage, the obtained 3D affine structure is upgraded to a Euclidean one with 4 GCPs. Experimental results on two public datasets demonstrate that the proposed method significantly outperforms several state-of-the-art methods in most cases.

UAV-BASED 3D VIRTUAL TOUR CREATION

Abstract. Lately, improvements in game engines have increased the interest in virtual reality (VR) technologies, that engages users with an artificial environment, and have led to the adoption of VR systems to display geospatial data. Because of the ongoing COVID-19 pandemic, and thus the necessity to stay at home, VR tours became very popular. In this paper, we tried to create a three-dimensional (3D) virtual tour for Gebze Technical University (GTU) Southern Campus by transferring high-resolution unmanned air vehicle (UAV) data into a virtual domain. UAV data is preferred in various applications because of its high spatial resolution, low cost and fast processing time. In this application, the study area was captured from different modes and altitudes of UAV flights with a minimum ground sampling distance (GSD) of 2.18 cm using a 20 MP digital camera. The UAV data was processed in Structure from Motion (SfM) based photogrammetric evaluation software Agisoft Metashape and high-quality 3D textured mesh models were generated. Image orientation was completed using an optimal number of ground control points (GCPs), and the geometric accuracy was calculated as ±8 mm (~0.4 pixels). To create the VR tour, UAV-based mesh models were transferred into the Unity game engine and optimization processes were carried out by applying occlusion culling and space subdivision algorithms. To improve the visualization, 3D object models such as trees, lighting poles and arbours were positioned on VR. Finally, textual metadata about buildings and a player with a first-person camera were added for an informative VR experience.

Influence of Control Points Configuration on the Mobile Laser Scanning Accuracy

Mobile mapping systems (MMS) are becoming widely used in standard geodetic tasks more commonly in the last years. The paper is focused on the influence of control points (CPs) number and configuration on mobile laser scanning accuracy. The mobile laser scanning (MLS) data was acquired by MMS RIEGL VMX-450. The resulting point cloud was compared with two different reference data sets. The first reference data set consisted of a high-accuracy test point field (TPF) measured by a Trimble R8s GNSS system and a Trimble S8 HP total station. The second reference data set was a point cloud from terrestrial laser scanning (TLS) using two Faro Focus3D X 130 laser scanners. The coordinates of both reference data sets were determined with significantly higher accuracy than the coordinates of the tested MLS point cloud. The accuracy testing is based on coordinate differences between the reference data set and the tested MLS point cloud. There is a minimum number of 6–7 CPs in our scanned area (based on MLS trajectory length) to achieve the declared relative accuracy of trajectory positioning according to the RIEGL datasheet. We tested two types of ground control point (GCP) configurations for 7 GCPs, using TPF reference data. The first type is a trajectory-based CPs configuration, and the second is a geometry-based CPs configuration. The accuracy differences of the MLS point clouds with trajectory-based CPs configuration and geometry-based CPs configuration are not statistically significant. From a practical perspective, a geometry-based CPs configuration is more advantageous in the nonlinear type of urban area such as our one. The following analyzes are performed on geometry-based CPs configuration variants. We tested the influence of changing the location of two CPs from ground to roof. The effect of the vertical configuration of the CPs on the accuracy of the tested MLS point cloud has not been demonstrated. The effect of the number of control points on the accuracy of the MLS point cloud was also tested. In the overall statistics using TPF, the accuracy increases significantly with increasing the number of GCPs up to 6. This number corresponds to a requirement of the manufacturer. Although further increasing the number of CPs does not significantly increase the global accuracy, local accuracy improves with increasing the number of CPs up to 10 (average spacing 50 m) according to the comparison with the TLS reference point cloud. The accuracy test of the MLS point cloud was divided into the horizontal accuracy test on the façade data subset and the vertical accuracy test on the road data subset using the TLS reference point cloud. The results of this paper can help improve the efficiency and accuracy of the mobile mapping process in geodetic praxis.

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).

Determining optimal photogrammetric adjustment of images obtained from a fixed‐wing UAV

AbstractPhotogrammetry with unmanned aerial vehicles (UAVs) has become a source of data with extensive applications. The accuracy is of utmost significance, yet the intention is also to find the best possible solutions for data acquisition in economic terms. The objective of the research was the analysis of various variants of the bundle block adjustment. The analysis concerns data which is diversified with respect to the type of shutter (rolling/global), the measurement of external orientation elements, the overlap and the number of ground control points (GCPs).

Evaluation On Different UAV’s Georeferencing Points to Generate Accurate Orthophoto and Digital Terrain Model

UAV or drone application of autonomy ranging can be divided into several levels, from basic hovering and position over trajectory tracking and waypoint navigation to fully autonomous navigation. This study used the DroneDeploy application for an autonomous flight mission. It is the process of taking photographs from an aircraft or other flying objects with a camera mounted on them to produce a three-dimensional (3D) map from the images captured, including a digital terrain model (DTM) and orthophotos. As for this study, the same output will be generated, but different flight parameter applications were used. Therefore, the study determined the optimum number of ground control points (GCPs) and evaluated the accuracy of the final results for each flight design. Acquired data were processed using the Pix4D modeller software due to the user-friendly factor and faster processing rate offered by the software. The results were analysed, and recommendations were made for future study improvement and to avoid similar problems. This study is useful for the mapping industry to achieve high accuracy results. 
 Keywords: Aerial photogrammetry, Flight Parameter, UAV apps, DTM, Orthophotos, GCPs

Assessing Repeatability and Reproducibility of Structure-from-Motion Photogrammetry for 3D Terrain Mapping of Riverbeds

Structure-from-Motion (SfM) photogrammetry is increasingly employed in geomorphological applications for change detection, but repeatability and reproducibility of this methodology are still insufficiently documented. This work aims to evaluate the influence of different survey acquisition and processing conditions, including the camera used for image collection, the number of Ground Control Points (GCPs) employed during Bundle Adjustment, GCP coordinate precision and Unmanned Aerial Vehicle flight mode. The investigation was carried out over three fluvial study areas characterized by distinct morphology, performing multiple flights consecutively and assessing possible differences among the resulting 3D models. We evaluated both residuals on check points and discrepancies between dense point clouds. Analyzing these metrics, we noticed high repeatability (Root Mean Square of signed cloud-to-cloud distances less than 2.1 cm) for surveys carried out under the same conditions. By varying the camera used, instead, contrasting results were obtained that appear to depend on the study site characteristics. In particular, lower reproducibility was highlighted for the surveys involving an area characterized by flat topography and homogeneous texturing. Moreover, this study confirms the importance of the number of GCPs entering in the processing workflow, with different impact depending on the camera used for the survey.

Multi-sensor point cloud data fusion for precise 3D mapping

Multi-sensor data fusion has recently gained a wide attention within the Geomatics research community, as it helps overcome the limitations of a single sensor and enables a complete 3D model for the structure and a better object classification. This study develops a data fusion algorithm, which optimally combines sensor data from a terrestrial and an unmanned aerial system (UAS) to obtain an improved and a complete 3D mapping model of a structure. Terrestrial laser scanner (TLS) data are collected for the exterior of a building along with the DJI Phantom 4 Pro and terrestrial close-range Sony α7R camera images. A number of ground control points and targets are established throughout the scanned building for the photogrammetric process and scans registration. Different point cloud datasets are generated from the TLS, UAS and the terrestrial Sony camera images. The created point clouds from each individual sensor and the fused point clouds are used in different forms, namely the original, denoised and subsampled point clouds. The denoised point cloud dataset is generated through the application of the statistical outlier remover (SOR) filter on the original point clouds. The relative precision of the 3D models is investigated using the multiscale model-to-model cloud comparison (M3C2) method. The TLS-based 3Dmodel is used as a reference. It is found that the precision of the Sony-based 3D model is higher than the other two models for the original and denoised datasets. The fused Sony/UAS-based model provides a complete 3D model with precision higher than the UAS-based model.

Influence of the Number and Spatial Distribution of Ground Control Points in the Accuracy of UAV-SfM DEMs: An Approach Based on Generalized Additive Models

The accuracy of digital elevation models (DEMs) obtained with unmanned aerial vehicle (UAV)-SfM photogrammetry depends on the quality, number, and distribution of ground control points (GCPs). In this work, generalized additive models (GAMs) are used to analyze the relationship between both, the root mean square error (RMSEz) and the mean absolute error (MAEz) in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Z$ </tex-math></inline-formula> coordinate, in a group of checkpoints and in a set of covariates related to the number and spatial distribution of the GCPs. Beyond the exploratory data analysis frequently used to analyze the effect of GCPs on UAV-generated DEM accuracy, our approach also allows the determination of the shape of the association between the response (RMSEz and MAEz) and the predictor variables describing GCP number and distribution when they are all combined in a single model. Among the different predictor variables studied, the number of GCPs has, by far, the greatest influence on vertical accuracy. Other variables such as mean distance between control points (CPs) or distance of checkpoints from their nearest CP are statistically significant but contribute much less to explaining RMSEz and MAEz. The analysis was performed by constructing 4600 DEMs from different GCP combinations, divided into four sampling methods: random, stratified, spatially balanced, and contour (edge) distribution. As expected, the random method produced the poorest results, while the stratified and contour distribution methods produced the smallest range and dispersion of errors.

Optimization of RFM Problem Using Linearly Programed ℓ₁-Regularization

Due to the existence of a large number of highly correlated parameters in the rational function model (RFM), known as rational polynomial coefficients (RPCs), RFM suffers from both ill-posedness and overparameterization problems. To tackle these problems, recently, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -regularized least-squares, in which <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> -norm of residuals and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -norm of RPCs are minimized, has been introduced. Nonetheless, RFM still suffers from these two phenomena, especially in the presence of a limited number of ground control points (GCPs). This study proposes a new parameter-free linear framework for RFM optimization. In this framework, called linearly programed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -regularized RFM framework (LPRFM), the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -norms of RPCs and residuals are simultaneously minimized through a linear objective function. To solve LPRFM as a linear optimization problem, the commonly used dual-simplex method is applied. LPRFM can be implemented for both RPC estimation and correction. Our experimental results indicated the superiority of the LPRFM against well-known competing methods in both estimation and correction of RPCs. The results showed that, in the estimation of RPCs, the proposed framework has led to accurate and robust results with only five GCPs. Moreover, accurate coordinates were obtained after correcting vendor-provided RPCs with only one GCP.

Controle estatístico de processo e padrão de exatidão cartográfico aplicado a aerolevantamento

Remotely piloted aircraft (RPA) are established in the market as a powerful tool for acquiring aerial images and facilitating mapping for various purposes. The aim of this study was to evaluate the quality of processes originating from the generation of georeferenced digital products employing a differing number of ground control points (GCP), using Statistical Process Control (SPC) and Mapping Accuracy Standards (MAS) in an orthomosaic produced with images from an RPA. A multirotor RPA was used to acquire aerial images over an area of 2 hectares. An orthomosaic was later generated using the PhotoScan software, and georeferenced with eight, five and three GCP (ground control points). Positioning errors were submitted to SPC to evaluate the quality of each process, and the orthomosaics were qualified by MAS. The results are promising, in view of the positioning errors of less than 0.1 m in the generated orthomosaics, which are classified as Mapping Accuracy Standards class ‘A’. Statistical Process Control showed acceptable levels of error, indicating the high accuracy of surveys of this nature. The precision obtained when mapping shows that aerial images obtained by means of RPA can be used in topographic surveys as long as error standards and process control are observed, attesting to the quality of the results.

Comparison of image georeferencing strategies for agricultural applications of small unoccupied aircraft systems

AbstractSmall unoccupied aircraft systems (sUAS) are becoming popular for mapping applications in agriculture, and photogrammetry software is available for developing orthorectified imagery and three‐dimensional surface models. Ground control points (GCPs), which are objects or locations with known geographic coordinates, may be required for accurate image georeferencing. However, few studies have compared global position equipment among sUAS or investigated the effects of GCP number or arrangement on georeferencing accuracy. The objectives of this study were to evaluate numbers and configurations of GCPs for georeferencing sUAS‐acquired images and determine the GCP requirements for sUAS with and without real‐time kinematic (RTK) global positioning equipment. The effects of varying numbers and configurations of GCPs were investigated on both a 0.40‐ha area the size of a typical plant breeding trial and a 64.7‐ha area (i.e., a U.S. quarter section) the size of a typical agricultural production field. Results demonstrated that four GCPs placed at the corners of the breeding‐scale field resulted in two‐dimensional (2D) error of ±3 cm in the absence of RTK, with minimal improvements when including more GCPs. The orthomosaics from the RTK‐equipped sUAS demonstrated improved 2D accuracy even without the use of GCPs, with a maximum mean error of 0.08 m. Four GCPs were found to be sufficient to reduce altitudinal (Z) error, with maximum mean error of only 0.05 and 1.98 m for the RTK and non‐RTK flights, respectively, for the production‐scale field. Thus, using four GCPs, RTK‐equipped sUAS, or a combination will result in improved georeferencing for photogrammetry products.

UAV PHOTOGRAMMETRY-BASED FOR OPEN PIT COAL MINE LARGE SCALE MAPPING, CASE STUDIES IN CAM PHA CITY, VIETNAM

The use of lightweight Unmanned Aerial Vehicle with the aerial photogrammetry approach to construct the Digital Surface Model (DSM) has been effectively applied for various types of topography. However, the ability to carry out this approach for huge active open coal mines is insufficiently investigated, furthermore, the influences of topographical factors on the accuracy of DSM are ambiguous. This experiment attempts to apply the UAV method for the two active coal mines with the total area of 7.99 km2 , exploited at a range from -300 m to 300 m altitude to figure out the effect of topographic factors on the accuracy of DEM constructed from UAV images. A total of 972 UAV images and 17 ground control points have been coupled to construct DSM of the mines. Besides, 16 checking points located at different elevations are used to evaluate the accuracy of DEM and to define the influence. DEMs are generated with the maximum RMSE of 0.086 m, 0.099 m, and 0.170 m corresponding to X, Y, and Z dimensional errors. The results show the unclear correlation between the vertical accuracy of DEM and the relative elevation (R2=0.064), the general slope of the mines, and the number of ground control points using in the coal mines as well.

Beach Leveling Using a Remotely Piloted Aircraft System (RPAS): Problems and Solutions

The size and great dynamism of coastal systems require faster and more automated mapping methods like the use of a remotely piloted aircraft system (RPAS) or unmanned aerial vehicle (UAV). This method allows for shorter intervals between surveys. The main problem for surveying using low-altitude digital photogrammetry in beach areas is their visual homogeneity. Obviously, the fewer the homologous points defined by the program, the lower the accuracy. Moreover, some factors influence the error performed in photogrammetric techniques, such as flight height, flight time, percentage of frame overlap (side and forward), and the number of ground control points (GCPs). A total of 72 different cases were conducted varying these factors, and the results were analyzed. Among the conclusions, it should be highlighted that the error for noon flights is almost double that for the early morning flights. Secondly, there is no appreciable difference regarding the side overlap. But, on the other side, RMSE increased to three times (from 0.05 to 0.15 m) when forward overlap decreased from 85% to 70%. Moreover, relative accuracy is 0.05% of the flying height which means a significant increase in error (66%) between flights performed at 60 and 100 m height). Furthermore, the median of the error for noon flights (0.12 m) is almost double that for the early morning flights (0.07 m) because of the higher percentage of grids with data for early flights. Therefore, beach levelings must never be performed at noon when carried out by RPAS. Eventually, a new parameter has been considered: the relationship between the number of GCPs and the surface to be monitored. A minimum value of 7 GCP/Ha should be taken into account when designing a beach leveling campaign using RPAS.

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

Aim. Determination of the elements of external spatial orientation of the surveying systems at the moment of image acquisition is the fundamental task in photogrammetry. Principally, this problem is solving in two ways. The first way is direct positioning and measuring of directions of camera optical axis in the geodetic space with the help of GNSS/INS equipment. The second way is the analytical solution of the problem using a set of reference information (often such information is a set of ground control points whose geodetic positions are known with sufficient accuracy and which are reliably recognised on aerial images of the photogrammetric block). The authors consider the task of providing reference and control information using the second approach, which has a number of advantages in terms of reliability and accuracy of determining the unknown image exterior orientation parameters. It is proposed to obtain additional images of ground control points by the method of their auxiliary aerial photography using an unmanned aerial vehicle (UAV) on a larger scale compared to the scale of the images of the photogrammetric block. The aim of the presented work is the implementation of the method of creating reference points and experimental confirmation of its effectiveness for photogrammetric processing. Methods and results. For the entire realization of the potential of the analytical way to determine the elements of external orientation of images, it is necessary to have a certain number of ground control points (GCP) and to keep the defined scheme of their location on the photogrammetric block. As the main source of input data authors use UAV aerial images of the terrain, which are obtained separately from the block of aerial survey, and have a better geometric resolution and which clearly depict the control reference points. Application of such auxiliary images gives the possibility of automated transferring of the position of ground control point into images of the main photogrammetric block. In our interpretation, these images of ground control points and their surroundings on the ground are called "control reference images". The basis of the work is to develop a method for obtaining the auxiliary control reference images and transferring of position of GCP depicted on them into aerial or space images of terrain by means of computer stereo matching. To achieve this goal, we have developed a processing method for the creation of control reference images of aerial image or a series of auxiliary multi-scale aerial images obtained by a drone from different heights above the reference point. The operator identifies and measures the GCP once on the auxiliary aerial image of the highest resolution. Then there is an automatic stereo matching of the control reference image in the whole series of auxiliary images in succession with a decrease in the resolution and, ultimately, directly with the aerial images of photogrammetric block. On this stage there are no recognition/cursor targeting by the human operator, and therefore there are no discrepancies, errors or mistakes related to it. In addition, if to apply fairly large size of control reference images, the proposed method can be used on a low-texture terrain, and therefore deal in many cases without the physical marking of points measured by GNSS method. And this is a way to simplify and reduce the cost of photogrammetric technology. The action of the developed method has been verified experimentally to provide the control reference information of the block of archival aerial images of the low-texture terrain. The results of the experimental approbation of the proposed method give grounds to assert that the method makes it possible to perform geodetic reference of photogrammetric projects more efficiently due to the refusal of the physical marking of the area before aerial survey. The proposed method can also be used to obtain the information for checking the quality of photogrammetric survey for provision of check points. The authors argue that the use of additional equipment - UAV of semi-professional class to obtain control reference images is economically feasible. Scientific novelty and practical relevance. The results of approbation of the "control reference image" method with obtaining stereo pairs of aerial images with vertical placement of the base are presented for the first time. There was implemented the study of the properties of such stereo pairs of aerial images to obtain images of reference points. The effectiveness of including reference images in the main block of the digital aerial triangulation network created on UAV’s images is shown.

Assessing the georeferencing accuracy of different amount of image stripes for linear UAV projects

At present, the results of photogrammetric processing of images obtained from UAVs (orthophoto mosaics, digital elevation models, etc.) are widely used for environmental studies. Such materials are especially relevant and in demand for environmental monitoring of hard-to-reach objects. In addition, UAV survey materials are indispensable for impact monitoring, in which observation, assessment and forecast of the state of the natural environment in areas where hazardous and potentially hazardous (NPP) sources of anthropogenic impact are located are carried out. Regardless of the method of georeferencing of images - direct or indirect - the accuracy of the generated product is evaluated by ground control points. The purpose of this study is to assess the accuracy of photogrammetric constructions depending on the number of strips when surveying linear objects from UAVs and on the number of control points used in indirect georeferencing. Five groups of experiments were carried out during the study, three in each group with a different number of strips (from one to three). Five groups are conventionally combined into two sections. In the first section, direct and indirect georeferencing techniques were used with three locally located control points. In the second section, the method of indirect georeferencing was used with a different number of ground control points: six, twelve and thirty-four. Estimates of the accuracy of various tests have shown that an increase in the number of strips does not always lead to an increase in accuracy.

High-Quality UAV-Based Orthophotos for Cadastral Mapping: Guidance for Optimal Flight Configurations

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows.

Discrete and Distributed Error Assessment of UAS-SfM Point Clouds of Roadways

Perishable surveying, mapping, and post-disaster damage data typically require efficient and rapid field collection techniques. Such datasets permit highly detailed site investigation and characterization of civil infrastructure systems. One of the more common methods to collect, preserve, and reconstruct three-dimensional scenes digitally, is the use of an unpiloted aerial system (UAS), commonly known as a drone. Onboard photographic payloads permit scene reconstruction via structure-from-motion (SfM); however, such approaches often require direct site access and survey points for accurate and verified results, which may limit its efficiency. In this paper, the impact of the number and distribution of ground control points within a UAS SfM point cloud is evaluated in terms of error. This study is primarily motivated by the need to understand how the accuracy would vary if site access is not possible or limited. In this paper, the focus is on two remote sensing case studies, including a 0.75 by 0.50-km region of interest that contains a bridge structure, paved and gravel roadways, vegetation with a moderate elevation range of 24 m, and a low-volume gravel road of 1.0 km in length with a modest elevation range of 9 m, which represent two different site geometries. While other studies have focused primarily on the accuracy at discrete locations via checkpoints, this study examines the distributed errors throughout the region of interest via complementary light detection and ranging (lidar) datasets collected at the same time. Moreover, the international roughness index (IRI), a professional roadway surface standard, is quantified to demonstrate the impact of errors on roadway quality parameters. Via quantification and comparison of the differences, guidance is provided on the optimal number of ground control points required for a time-efficient remote UAS survey.

Methodology of Processing Single-Strip Blocks of Imagery with Reduction and Optimization Number of Ground Control Points in UAV Photogrammetry

Unmanned aerial vehicle (UAV) systems are often used to collect high-resolution imagery. Data obtained from UAVs are now widely used for both military and civilian purposes. This article discusses the issues related to the use of UAVs for the imaging of restricted areas. Two methods of developing single-strip blocks with the optimal number of ground control points are presented. The proposed methodology is based on a modified linear regression model and an empirically modified Levenberg–Marquardt–Powell algorithm. The effectiveness of the proposed methods of adjusting a single-strip block were verified based on several test sets. For method I, the mean square errors (RMSE) values for the X, Y, Z coordinates of the control points were within the range of 0.03–0.13 m/0.08–0.09 m, and for the second method, 0.03–0.04 m/0.06–0.07 m. For independent control points, the RMSE values were 0.07–0.12 m/0.06–0.07 m for the first method and 0.07–0.12 m/0.07–0.09 m for the second method. The results of the single-strip block adjustment showed that the use of the modified Levenberg–Marquardt–Powell method improved the adjustment accuracy by 13% and 16%, respectively.