Distribution Of Ground Control Points Research Articles

The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation

The vast potential of high-resolution satellite images, including Kompsat-3A, has been demonstrated across diverse applications, such as mapping and disaster monitoring. However, these images can only be utilized as reliable GIS (geographic information system) data when they possess precise geographical information. To achieve this, sensor model information, represented by RPCs (rational polynomial coefficients), requires bias compensation through GCPs (ground control points). Though having a substantial number of well-distributed GCPs across satellite images is ideal, the acquisition process is often restricted due to cost and inaccessibility. The uniform distribution of GCP chips is not guaranteed, necessitating an investigation into the impact of GCP distribution on the bias compensation process, which is the focus of this study. Experiments were meticulously conducted using Kompsat-3A data using dense GCP information. The dense GCP information was automatically generated from aerial orthoimages through a three-step process. Firstly, the GCP chips were extracted from the aerial images, focusing on feature points. Secondly, these chips were projected onto the target Kompsat-3A data to align them accurately. Lastly, precise satellite image coordinates of the chips were obtained through image matching between the chips and the target Kompsat-3A image. The dense GCPs enabled detailed bias analysis that exhibited skewness in most Kompsat-3A data. This necessitates the implementation of an affine model for proper bias compensation over the entire image space. Next, the study delved into the influence of GCP distribution on RPC bias compensation. To this end, each target satellite image space was divided into nine zones, with the dense GCPs assigned accordingly. The accuracy of bias compensation was analyzed across nine experimental cases, ranging from GCPs occupying only one zone to GCPs covering all nine zones. It was observed that GCPs covering at least four or five zones should be utilized for reliable RPC bias compensation of Kompsat-3A, especially when aiming for a high level of accuracy with an RMSE of one pixel. Finally, it was concluded that GCPs covering three zones yielded satisfactory results as a minimum GCP requirement, but this was contingent upon their distribution not following a straight zone pattern.

Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production

Unmanned aerial vehicles (UAVs) have been employed to perform aerial surveys in many industries owing to their versatility, relatively low cost, and efficiency. Ground control points (GCPs) are used for georeferencing to ensure orthophoto geolocation/positioning accuracy. In this study, we investigate the impact of the number and distribution of GCPs on the accuracy of orthophoto production based on images acquired by UAVs. A test site was selected based on regulatory requirements, and several scenarios were developed considering the specifications of the UAVs used in this study. The locations of GCPs were varied to obtain the results. Based on the results obtained for different numbers of GCPs per unit area and distribution of GCPs, it is shown that UAV-based platforms can be more extensively utilized in a range of applications. The findings of this study will significantly impact the development process of GCP automation algorithms and enable a more cost-effective approach when determining target sites for UAV-based orthophoto production.

Optimal camera focal length detection method for GPS-supported bundle adjustment in UAV photogrammetry

The global positioning system (GPS) supported bundle adjustment (BA) method of unmanned aerial vehicle (UAV) photogrammetry significantly minimises the number of ground control points (GCPs); however, there are often systematic errors in the vertical axis direction of the 3D reconstruction results. In this study, a method that relies only on a small number of GCPs for detecting and estimating the optimal focal length of the camera is proposed. First, the scene structure information is obtained based on the conventional GPS-supported BA optimisation method, and the camera focal length is redefined based on a single GCP. Second, a second-order polynomial is used to fit the redefined camera focal lengths of multiple GCPs and the corresponding positioning accuracy. The highest accuracy position is selected as the optimal focal length of the camera. Finally, the optimal focal length of the camera is taken as a constant, and the conventional GPS-supported BA method is used to complete the optimisation again. The experimental results show that under the same number and distribution of GCPs, the camera detection method’s total positioning accuracy proposed in this paper is improved by an average of 37.64%. The optimal camera focal length obtained by the method can correct the systematic error in the vertical direction while keeping the vertical accuracy consistent with the horizontal accuracy.

Enhancement of low-cost UAV-based photogrammetric point cloud using MMS point cloud and oblique images for 3D urban reconstruction

The accurate and dense reconstruction of high-quality 3D spatial information is essential for digital twin-based smart cities. Unmanned aerial vehicle (UAV)-based photogrammetry allows the 3D modeling of urban environments in the shortest possible time. Accurate georeferencing is a prerequisite for utilizing geospatial information. The geolocation accuracy of real-time kinematic (RTK)/post-processing kinematic (PPK) UAV-based photogrammetry is significantly high; however, RTK/PPK UAVs are costly. In contrast, the geolocation accuracy of low-cost UAV-based photogrammetry is generally low. It can be improved by indirect georeferencing using ground control points (GCPs) obtained in situ; however, this requires significant amounts of human resources and time. Therefore, this study analyzes the suitability of utilizing the mobile mapping system (MMS) point cloud as GCPs for low-cost UAV-based photogrammetry. We checked the significance of the vertical distribution of GCPs on the geolocation accuracy of low-cost UAV-based photogrammetry using the feature points of building facades (captured via vehicle-based MMS along roads) as GCPs. In addition, typical UAV-based photogrammetry only uses nadir images, which limits the detailed 3D reconstruction of building facades. In this study, a detailed reconstruction and an improved vertical geolocation accuracy were achieved using oblique images. Experiments demonstrated that the geolocation accuracy of the low-cost UAV-based photogrammetric point cloud improved to within 16 cm in the X-, Y-, and Z-directions. It was at its highest when the GCPs were diversely distributed in the vertical direction. Finally, we generated an enhanced point cloud by merging the low-cost UAV-based photogrammetric point cloud and the MMS point cloud.

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

Improving UAV‐SfM photogrammetry for modelling high‐relief terrain: Image collection strategies and ground control quantity

AbstractImage collection strategies and ground control points (GCPs) are of particular importance for uncrewed aerial vehicle combined with Structure‐from‐Motion (UAV–SfM) photogrammetry, and the generalization of their effects has proved elusive. This study designed various photogrammetric scenarios to investigate the effects of image collection strategies, ground control quantity, and their interaction on digital elevation model (DEM) errors and their spatial structure in high‐relief terrain. The results of 1.77 × 105 UAV–SfM scenarios provide insights for improving UAV–SfM practices. A high image capture angle (20–40°) enhances camera calibration quality decreasing the magnitude and spatial correlation of errors. High camera inclination reduces the sensitivity of mean and standard deviation of error to flying height but not the spatial correlation of error. Including additional data (e.g. supplemented convergent images; images captured at multiple flying heights) has only a minor effect if imagery is highly inclined. GCPs provide more effective constraints than image collection strategies. The mean error and standard error decline quickly with a small number of GCPs and then become stable in all scenarios, but the spatial correlation of error can be further improved with increasing GCPs. However, the effects of GCP quantity do interact with image collection strategies. High camera inclination reduces requirements for GCPs, whilst strategies combining different flying heights and image orientations have little effect on necessary GCP quantity. The distribution of GCPs still affects the errors, but the effect of GCP distribution becomes less important with an increase in the number of GCPs. Finally, we show that UAV–SfM photogrammetric quality assessment should routinely assess the spatial dependence of error using a statistic like Moran's I.

Towards a Guideline for UAV-Based Data Acquisition for Geomorphic Applications

Recent years have seen a rapid rise in the generation of high-resolution topographic data using custom-built or commercial-grade Unmanned Aerial Vehicles (UAVs). Though several studies have demonstrated the application potential of UAV data, significant knowledge gaps still exist in terms of proper documentation of protocols for data acquisition, post-flight data processing, error assessments, and their mitigation. This work documents and provides guidelines for UAV data acquisition and processing from several years of field experience in diverse geomorphic settings across India, including undulating topography (~17 km2), alluvial plains (~142 km2), lowland-river basin (~66 km2), and a highly urbanized area (~5 km2). A total of 37,065 images with 16 and 20 Megapixels and 604 ground control points (GCPs) were captured with multiple UAV systems and processed to generate point clouds for a total area of ~230 km2. The Root Mean Square Error (RMSE) for each GCP for all sites ranged from 6.41 cm to 36.54 cm. This manuscript documents a comprehensive guideline for (a) pre-field flight planning and data acquisition, (b) generation and removal of noise and errors of the point cloud, and (c) generation of orthoimages and digital elevation models. We demonstrate that a well-distributed and not necessarily uniformly distributed GCP placement can significantly reduce doming error and other artifacts. We emphasize the need for using separate camera calibration parameters for each flight and demonstrate that errors in camera calibration can significantly impact the accuracy of the point cloud. Accordingly, we have evaluated the stability of lens calibration parameters between consumer-grade and professional cameras and have suggested measures for noise removal in the point cloud data. We have also identified and analyzed various errors during point cloud processing. These include systematic doming errors, errors during orthoimage and DEM generation, and errors related to water bodies. Mitigation strategies for various errors have also been discussed. Finally, we have assessed the accuracy of our point cloud data for different geomorphic settings. We concluded that the accuracy is influenced by Ground Sampling Distance (GSD), topographic features, and the placement, density, and distribution of GCPs. This guideline presented in this paper can be extremely beneficial to both experienced long-term users and newcomers for planning the UAV-based topographic survey and processing the data acquired.

Feasibility of Accurate Point Cloud Model Reconstruction for Earthquake-Damaged Structures Using UAV-Based Photogrammetry

Camera-enabled unmanned aerial vehicles (UAVs) provide a promising technique to considerably speed up the inspection and visual data collection from regions that may otherwise be inaccessible. In addition, the technology of image-based 3D reconstruction can generate a point cloud model using images captured by UAVs. However, the performance of the point cloud modeling may be affected by multiple factors, such as the modeling software, ground control points (GCPs), and UAV flight modes. In this study, three common software packages were compared, and Pix4Dmapper was considered a suitable software for point cloud modeling for earthquake-damaged buildings. The accuracy and resolution of point cloud models are usually evaluated by root mean square error (RMSE) and ground sampling distance (GSD). The effects of the main factors, including the number of GCPs, distribution of GCPs, flight manner of the UAV, and distance from the UAV to the target, were investigated on the basis of two real-world multistory earthquake-damaged structures. The influence rules of the main factors revealed that a close range, automatic flight mode of the UAV, a large number of GCPs, and a relatively wide distribution of the GCPs may generate a point cloud model with low computational costs, high accuracy, and high resolution. In the particular illustration example here, the RMSE is 6.78 mm while the GSD is 1.60 mm. Finally, rapid structural damage inspection was demonstrated using an accurate point cloud model and compared with the inspection results of a total station and terrestrial laser scanner point cloud models. The comparison of different inspection results showed that the relative errors were relatively acceptable within 4%.

ASSESSING THE VIABILITY OF PPK TECHNIQUES FOR ACCURATE MAPPING WITH UAS

Abstract. Utilizing ground control points (GCPs) to georeference photogrammetry-based point cloud data is a common practice in unmanned aerial system (UAS) mapping. Direct georeferencing or integrated sensor orientation (ISO) can be used to obtain georeferenced point clouds from UAS without relying heavily on GCPs. However, the accuracy of the point cloud may be impacted by the accuracy of the trajectory solution obtained by GNSS. To improve point cloud accuracy, post-processing kinematic (PPK) solutions can be applied to the UAS trajectory, which may provide higher accuracy than low-accuracy trajectory solutions and minimize the reliance on GCPs. This study compares the accuracy and precision of two different point clouds generated using different methods. One point cloud was generated using traditional photogrammetric methods with low accuracy Global Navigation Satellite System (GNSS) observations from the UAS and GCPs that have an average accuracy of one to two centimeters, while the other was generated using PPK trajectory solution for the UAS’s trajectory with two software: open-source Emlid Studio and the widely used Inertial Explorer. The use of PPK techniques in UAS mapping may have several potential benefits over traditional methods. By correcting the errors in the UAS's trajectory, a user may only need to depend on fewer ground control points, which can reduce the time and cost associated with fieldwork. This is particularly useful in areas that are difficult to access or have limited ground control point options, such as in urban or forested areas. To evaluate performance, a GNSS receiver is used to obtain measurements on checkpoints, which are used to assess the accuracies of the point clouds. In our experiments, the accuracy of the point clouds generated using PPK trajectory solution with high accuracy GCPs was found to be higher than those generated with low accuracy GNSS observations while aided with high accuracy ground control points. While the use of PPK with GCPs is generally expected to provide more accurate and reliable data than low-accuracy GNSS observations even after adjusting with GCPs, the number and distribution of GCPs can still significantly impact overall accuracy. Therefore, careful consideration of the number of GCPs and their placement is essential to achieve the desired level of efficiency and effectiveness in UAS mapping.

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.

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.

A Novel DEM Block Adjustment Method for Spaceborne InSAR Using Constraint Slices.

The lack and uneven distribution of Ground Control Points (GCPs) will lead to the deterioration of Digital Elevation Model (DEM) block adjustment results in the bistatic Interferometric Synthetic Aperture Radar (InSAR) system. Given this issue, we first explain the relationship between the stability of adjustment parameters and the GCP distribution pattern theoretically using matrix perturbation theory. Second, we put forward the Constraint Slices (CSs) concept and first introduce CSs into the adjustment optimization model as constraint conditions rather than actual values as GCPs. Finally, we propose a novel DEM block adjustment method for spaceborne InSAR using CSs based on an optimization model with nonlinear constraints. The simulated experiment shows the instability of the conventional method and validates the proposed method under different parallel baseline errors. Four groups of real experiments were carried out according to the size of the uncontrolled area using twelve Co-registered Single-look Slant–range Complex (CoSSC) datasets for Henan Province, China. The adjustment results verified by the ICESat-2 ATL08 data demonstrate that the performance of the proposed method is better than the conventional method in the uncontrolled area; the corresponding improvements in adjustment accuracies compared with the conventional method are 0.13 m, 1.02 m, 2.12 m, and 8.18 m, respectively. At the same time, the proposed method can enhance the height consistency in overlapping areas, which is vital for seamless DEM production.

A TIR-Visible Automatic Registration and Geometric Correction Method for SDGSAT-1 Thermal Infrared Image Based on Modified RIFT

High-resolution thermal infrared (TIR) remote sensing images can more accurately retrieve land surface temperature and describe the spatial pattern of urban thermal environment. The Thermal Infrared Spectrometer (TIS), which has high spatial resolution among spaceborne thermal infrared sensors at present, and global data acquisition capability, is one of the sensors equipped in the SDGSAT-1. It is an important complement to the existing international mainstream satellites. In order to produce standard data products, rapidly and accurately, the automatic registration and geometric correction method needs to be developed. Unlike visible–visible image registration, thermal infrared images are blurred in edge details and have obvious non-linear radiometric differences from visible images, which make it challenging for the TIR-visible image registration task. To address these problems, homomorphic filtering is employed to enhance TIR image details and the modified RIFT algorithm is proposed to achieve TIR-visible image registration. Different from using MIM for feature description in RIFT, the proposed modified RIFT uses the novel binary pattern string to descriptor construction. With sufficient and uniformly distributed ground control points, the two-step orthorectification framework, from SDGSAT-1 TIS L1A image to L4 orthoimage, are proposed in this study. The first experiment, with six TIR-visible image pairs, captured in different landforms, is performed to verify the registration performance, and the result indicates that the homomorphic filtering and modified RIFT greatly increase the number of corresponding points. The second experiment, with one scene of an SDGSAT-1 TIS image, is executed to test the proposed orthorectification framework. Subsequently, 52 GCPs are selected manually to evaluate the orthorectification accuracy. The result indicates that the proposed orthorectification framework is helpful to improve the geometric accuracy and guarantee for the subsequent thermal infrared applications.

Accuracy Assessment of a UAV Direct Georeferencing Method and Impact of the Configuration of Ground Control Points

Unmanned aerial vehicles (UAVs) can obtain high-resolution topography data flexibly and efficiently at low cost. However, the georeferencing process involves the use of ground control points (GCPs), which limits time and cost effectiveness. Direct georeferencing, using onboard positioning sensors, can significantly improve work efficiency. The purpose of this study was to evaluate the accuracy of the Global Navigation Satellite System (GNSS)-assisted UAV direct georeferencing method and the influence of the number and distribution of GCPs. A FEIMA D2000 UAV was used to collect data, and several photogrammetric projects were established. Among them, the number and distribution of GCPs used in the bundle adjustment (BA) process were varied. Two parameters were considered when evaluating the different projects: the ground-measured checkpoints (CPs) root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2) distance. The results show that the vertical and horizontal RMSE of the direct georeferencing were 0.087 and 0.041 m, respectively. As the number of GCPs increased, the RMSE gradually decreased until a specific GCP density was reached. GCPs should be uniformly distributed in the study area and contain at least one GCP near the center of the domain. Additionally, as the distance to the nearest GCP increased, the local accuracy of the DSM decreased. In general, UAV direct georeferencing has an acceptable positional accuracy level.

IMPROVING THE INTERNAL ACCURACY OF UAV-IMAGE BLOCKS USING LOCAL LOW-ALTITUDE FLIGHTS AND SCALE-BARS

Abstract. Accurate image-based measurement based on UAV data is attracting attention in various applications. While the external accuracy of the UAV image blocks could be mainly affected by object-space information like number and distribution of ground control points and RTK-GNSS accuracy, its internal accuracy highly depends on camera specifications, flight height, data capturing setup and accuracy of scale estimation. For many small-scale projects accurate local measurements are highly demanded. This necessitates high internal accuracy of images block which could be transferred from model space to object space by accurate estimation of the scale parameter. This research aims at improving the internal accuracy of UAV image blocks using low-altitude flight(s) over small parts of the project area without using any ground control points. Possible further improvement by using calibrated scale-bars which serve as scale-constraints is also investigated. To this end, different scenarios of the flight configuration and distance measurements in the two photogrammetric blocks are also considered and the results are analyzed. Our investigations show 50% accuracy improvement achieved by performing local flights over small parts of the scene, given that RTK information is available. Moreover, adding accurate scale-bars increased the accuracy improvement to 67%. Furthermore, when RTK information is not available, adding local low-altitude flights and scale-bars decrease the error of local distance measurement form 1–3 meters to less than 4 centimeters.

Sensitivity of Enhanced SRTM for Sea-Level Rise Variation on Egyptian Coasts

The accuracy of digital elevation models (DEMs) is very critical to planning adaptation strategies for coastal areas under changing scenarios of global climate and sea-level rise. This research assesses the accuracy of the interferometric DEM from the SRTM mission as it is one of the free available DEMs that have been widely used in many applications. The SRTM model is enhanced using the best fit global geoid model for Egypt instead of EGM96 and by applying a mathematical scale factor formula. This research aims to obtain a higher vertical accuracy of the SRTM model that achieves the requirements for coastal inundation studies with minimum field measurement data. The methodology approach consists of three phases. In the first phase, the enhancement scale factor formulas were derived using uniformly distributed ground control points (GCPs) at two different terrains data in Egypt. The second phase contains the evaluation and validation process. It was observed that the enhanced accuracy achieved ranged from 45% to 60 % based on the type of terrains. In the last phase, the sensitivity of these DEMs to sea-level projections using the recent available local tide gauge data was analyzed. It is recommended to investigate this method approach to determine the optimal size and distribution of reference ground control points needed to adjust various free open DEMs of low vertical accuracy for further studies and other applications.

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.

Influence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle

ABSTRACT This study aimed to explore the influence of flight altitude, density, and distribution of ground control points (GCPs) on the digital terrain model (DTM) in surveys conducted by unmanned aerial vehicles (UAVs). A total of 144 photogrammetric projects consisting of 399 aerial photos were carried out in a 2 ha area. These photogrammetric projects involved six GCP distributions (edge, center, diagonal, parallel, stratified, and random), six GCP densities, and four flight altitudes (30, 60, 90, and 120 m). The response surface methodology was used to find interference factors and total root-mean-square error (RMSEt) as well. The 60 m flight altitude presented was the most efficient. Central GCP distribution was observed to have low precision. Using stratified and random edge distributions, 10 GCPs are recommended to achieve geometric precision below 0.07 m at any flight height. However, for studies requiring up to 0.07 m precision, the best distribution was parallel with 4 GCPs at any altitude. Diagonal positioning of the GCPs showed RMSEt values below 0.11 m with 4 GCPs at any altitude. A good distribution of GCPs was found to be important, but the density of GCPs per image was more relevant when obtaining a lower RMSEt.

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.