Ground control points and their influences on the precision of generating a digital surface model using an unmanned aerial vehicle

Unmanned aerial vehicle (UAV) photogrammetry has recently emerged as a popular solution to obtain certain products necessary in linear projects, such as orthoimages or digital surface models. This is mainly due to its ability to provide these topographic products in a fast and economical way. In order to guarantee a certain degree of accuracy, it is important to know how many ground control points (GCPs) are necessary and how to distribute them across the study site. The purpose of this work consists of determining the number of GCPs and how to distribute them in a way that yields higher accuracy for a corridor-shaped study area. To do so, several photogrammetric projects have been carried out in which the number of GCPs used in the bundle adjustment and their distribution vary. The different projects were assessed taking into account two different parameters: the root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2). From the different configurations tested, the projects using 9 and 11 GCPs (4.3 and 5.2 GCPs km−1, respectively) distributed alternatively on both sides of the road in an offset or zigzagging pattern, with a pair of GCPs at each end of the road, yielded optimal results regarding fieldwork cost, compared to projects using similar or more GCPs placed according to other distributions.

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.

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

Unmanned Aerial Vehicle (UAV) based photogrammetry is becoming a valuable source of data for topographic mapping, volume calculations, terrain mapping, and generating 3D models. However, the use of UAVs for any purpose requires basic knowledge of various flight settings. The number and distribution of Ground Control Points (GCPs) are the most crucial, therefore, the number of GCPs should be used economically. This paper discusses the accuracy of UAV based photogrammetric products in Corridor mapping and area with Undulating terrain for different sets of flight settings. Influence of number of GCPs and their distribution patterns are assessed for optimal accuracy. For the accuracy assessment of GCP distribution, various configurations of GCPs were tested. The acquired accuracy was then compared for each of these configurations and the most suitable ones were determined for each terrain type. In corridor mapping, the distribution of GCPs depends upon the length of an area with GCPs alternating each side of the linear feature, separated by an offset distance along with the feature. In our study, the optimum number of GCPs was found to be four, along with the feature being mapped. Similarly, in the area with undulating terrain, the GCPs should be established in places covering all elevations with a minimum of five GCPs in shape of a die. Our results show that distribution and number of GCP used during UAV based survey play a major role in the accuracy of Digital Surface Model (DSM) and orthomosaics. The accuracy not only depends upon the number of GCPs but also on its distribution pattern. Therefore, the choice of suitable pattern and number of GCPs for a particular mission can help obtain results with desired accuracy as well as economic feasibility.

&amp;lt;p&amp;gt;How to use a suitable method to accurately measure gully morphology is very important in the study of gully erosion monitoring and development, and the development of Unmanned Aerial Vehicle (UAV) has made it easy to apply UAV photogrammetry techniques to gully erosion studies. The aim of this study is to evaluate the accuracy of data and the efficiency of data processing by analyzing the errors of different schemes, and to provide suitable plan design ideas for the study of gully by UAV. Gully is the object of study and different flight schemes and Ground Control Point (GCP) placement schemes are used to acquire and process the data, and finally the errors are analyzed by Digital Surface Model (DSM) and orthophoto. Among all the schemes, the one with a flight altitude of 30m, 80%/70% photo overlap and 11 GCPs had the highest accuracy (Mean absolute error of 0.0353m and root mean square error of 0.0525m), but this scheme took more data collection and processing time and was less efficient. The number of GCPs and the placement location also have a significant impact on the accuracy&amp;amp;#65292;the position closer to the GCPs has a smaller error&amp;amp;#65292;and this study proves that the number of GCPs should not be more than 9 and should be evenly distributed in different parts of the gully.. When the flight altitude is 70m, the overlap is not less than 50%/40%, and the number of control points is 6, both accuracy and measurement efficiency can be taken into account at the same time. In addition, the sources of errors and the distribution locations of checkpoints with high errors were analyzed in four aspects: shadow, slope gradient, slope direction and vegetation. The use of UAVs in gully erosion studies is very convenient to get the later products with centimeter-level accuracy, and based on the results of the study we suggest that the flight altitude and photo overlap can be appropriately reduced when designing the scheme, and the number of GCP can be increased in the areas that need to be focused on and the areas with large elevation changes. At the same time, flight safety, UAV battery power, data collection efficiency and processing efficiency should be considered comprehensively.&amp;lt;/p&amp;gt;

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.

This paper explores the influence of flight altitude, terrain morphology, and the number of ground control points (GCPs) on digital surface model (DSM) and orthoimage accuracies obtained with unmanned aerial vehicle (UAV) photogrammetry. For this study, 60 photogrammetric projects were carried out considering five terrain morphologies, four flight altitudes (i.e., 50, 80, 100, and 120 m), and three different numbers of GCPs (i.e., 3, 5, and 10). The UAV was a rotatory wing platform with eight motors, and the sensor was a nonmetric mirrorless reflex camera. The root-mean-square error (RMSE) was used to assess the accuracy of the DSM (Z component) and orthophotos (X, Y, and XY components RMSEX, RMSEY, and RMSEXY, respectively). The results show that RMSEX, RMSEY, and RMSEXY were not influenced by flight altitude or terrain morphology. For horizontal accuracy, differences between terrain morphologies were observed only when 5 or 10 GCPs were used, which were the best accuracies for the flattest morphologies. Nevertheless, the number of GCPs influenced the horizontal accuracy; as the number of GCPs increased, the accuracy improved. Vertical accuracy was not influenced by terrain morphology, but both flight altitude and the number of GCPs had significant influences on RMSEZ; as the number of GCPs increased, the accuracy improved. Regarding flight altitude, vertical accuracy decreased as flight altitude increased. The most accurate combination of flight altitude and number of GCPs was 50 m and 10 GCPs, respectively, which yielded RMSEX, RMSEY, RMSEXY, and RMSEZ values equal to 0.038, 0.035, 0.053, and 0.049 m, respectively. In view of these results, the map scale according to the legacy American Society for Photogrammetry and Remote Sensing map standard of 1990 will be approximately 1:150, and an equivalent contour interval of 0.150 m is sufficient for most civil engineering projects.

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.

The optimization of an unmanned aerial vehicle (UAV) aerial photogrammetry scheme is crucial for achieving higher precision mapping results. Three representative factors, namely the real-time kinematic (RTK) mode, flight altitude, and the number of ground control points (GCPs) were selected to analyze their impact on UAV aerial photogrammetry accuracy. Four flight altitude tests were conducted separately in two RTK modes, and five GCP layout schemes were designed. Based on this, the root mean square error (RMSE) values of 40 aerial photogrammetric results were analyzed. The results showed a significant correlation between flight altitude and resolution of the UAV aerial photogrammetric results. Further, conversion formulas between actual image resolution and flight altitude for different GCP values were also derived in RTK and non-RTK modes. In the case of precise positioning, the horizontal and vertical accuracy of the aerial photogrammetric image decreased with increasing flight altitude. Under the same flight altitude, the addition or no addition of GCPs, including changes in GCP numbers, had no significant effect on improving the accuracy of aerial photogrammetry in RTK mode. However, in non-RTK mode, the number of GCPs significantly affected accuracy. The horizontal and vertical RMSE values decreased rapidly with the increase in GCP numbers and then stabilized. However, regardless of whether RTK was activated, an excessive number of GCPs was not conducive to improving the accuracy of aerial photogrammetric results. The mapping accuracy of UAVs in RTK mode without GCPs was equivalent to that in non-RTK mode with GCPs. Therefore, when using RTK-UAVs, deploying GCPs is unnecessary under suitable circumstances. Finally, practical suggestions for optimizing the UAV aerial photogrammetry scheme are provided as a reference for related applications.

Coal production in opencast mining generates substantial waste materials, which are typically delivered to an on-site waste dump. As a large artificial loose pile, such dumps have a special multi-berm structure accompanied by some security issues due to wind and water erosion. Highly accurate digital surface models (DSMs) provide the basic information for detection and analysis of elevation change. Low-cost unmanned aerial vehicle systems (UAS) equipped with a digital camera have become a useful tool for DSM reconstruction. To achieve high-quality UAS products, consideration of the number and configuration of ground control points (GCPs) is required. Although increasing of GCPs will improve the accuracy of UAS products, the workload of placing GCPs is difficult and laborious, especially in a multi-berm structure such as a waste dump. Thus, the aim of this study is to propose an improved GCPs configuration to generate accurate DSMs of a waste dump to obtain accurate elevation information, with less time and fewer resources. The results of this study suggest that: (1) the vertical accuracy of DSMs is affected by the number of GCPs and their configuration. (2) Under a set number of GCPs, a difference of accuracy is obtained when the GCPs are located on different berms. (3) For the same number of GCPs, the type 4 (GCPs located on the 1st and 4th berms) in the study is the best configuration for higher vertical accuracy compared with other types. The principal objective of this study provides an effective GCP configuration for DSM construction of coal waste dumps with four berms, and also a reference for engineering piles using multiple berms.

Georeferenced archival aerial images are key elements for the study of landscape evolution in the scope of territorial planning and management. The georeferencing process proceeds by applying to photographs advanced digital photogrammetric techniques integrated along with a set of ground truths termed ground control points (GCPs). At the end of that stage, the accuracy of the final orthomosaic is assessed by means of root mean square error (RMSE) computation. If the value of that index is deemed to be unsatisfactory, the process is re-run after increasing the GCP number. Unfortunately, the search for GCPs is a costly operation, even when it is visually carried out from recent digital images. Therefore, an open issue is that of achieving the desired accuracy of the orthomosaic with a minimal number of GCPs. The present paper proposes a geostatistically-based methodology that involves performing the spatialization of the GCP errors obtained from a first gross version of the georeferenced orthomosaic in order to produce an error map. Then, the placement of a small number of new GCPs within the sub-areas characterized by the highest local errors enables a finer georeferencing to be achieved. The proposed methodology was applied to 67 historical photographs taken on a geo-morphologically complex study area, located in Southern Italy, which covers a total surface of approximately 55,000 ha. The case study showed that 75 GCPs were sufficient to garner an orthomosaic with coordinate errors below the chosen threshold of 10 m. The study results were compared with similar works on georeferenced images and demonstrated better performance for achieving a final orthomosaic with the same RMSE at a lower information rate expressed in terms of nGCPs/km2.

The Selection of ground control points (GCP) for remote sensing image geometric rectification is an important step. The number, distribution and accuracy of GCP have a direct impact on the effect of geometric rectification. With the development of remote sensing technology, GCP auto-matching algorithm automatically has access to many high precision GCP. However, very few studies of the distribution of GCP, which are also important to geometric rectification, are carried on. GCP should be evenly distributed in the whole image. However the understanding of evenly distribution has high subjectivity. In this paper, the method based on cluster analysis has been proposed to optimize the distribution of GCP. A subset of appropriate, even and high-precision GCP was filtered from a large number of GCP. Through the introduction of the concept of the monopolized circle, the uniform index was put forward to measure the uniformity of GCP pattern quantitatively. This paper also studied the relationship between number and precision of GCP. It is proved by experiment that the rest GCP after the algorithm of optimization were evenly distributed and achieved good results. At the same time, the efficiency and accuracy of image geometric rectification could be improved.

Orthomosaic is a map derived from numbers of overlapped aerial images that are stitched together using photogrammetry software. Orthomosaic can be used as an analysing tool to extract topographic features and monitoring applications. The accuracy of any photogrammetry product is important and can be dictated by various reasons. One of the prominent factors is the utilization of Ground Control Point (GCP). GCPs is known as sets of points in the study area that were established to provide aerial images with known coordinates through georeferencing process. The optimum GCP distribution is at the edge of the study area. However, it is sometimes impossible to access several areas of the mapping area due to unexpected prohibitions. The only option is to establish the point at the area where they can access it. Different numbers and distribution of GCP will contribute to the changes in accuracy. For this study, twelve points were established using GNSS observation. These points are utilized as either GCP or Check Point (CP). The number of GCP for each processing differs from three GCPs to six GCPs. Each number of GCP were distributed either at the edge or centre of the area. Edge distribution is supported with several GCPs at the centre. This study utilized a multi-rotor drone with an integrated camera for data acquisition. The method of analysis for this study is by comparing the coordinates of CP and computing the planimetric and vertical RMSE from each orthomosaic. This study found that the best GCP configuration with optimum accuracy is six GCPs with edge distribution with the RMSE of 2.9 cm and 4.4 cm for planimetric and vertical accuracy, respectively. This study helps to plan the task to establish the GCPs thoroughly to ensure great accuracy can be achieved regardless of the circumstances.

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).

Recently, close-range terrestrial digital photogrammetry has received increased attention in geomorphological studies due to high image resolution, sufficient accuracy, and cost efficiency compared to the other techniques. Digital photogrammetry can be described as a non-contacting remote sensing technique that facilitates earth surface reconstruction and provides digital coordinates of the earth points and geospatial features based on a series of overlapping images. It uses Structure-from-Motion and Multi-view Stereo algorithms to recover 3-D features from a projected 2-D scene of the collected images. The final products are 3-D point cloud and digital surface model (DSM). This study focused on evaluating the accuracy of the developed DSM for an artificial channel in a 4 m x 1 m soil bed manufactured in a controlled lab environment (Figure 1). <fig><graphic xlink:href=23028_files/23028-08.jpg id=ID_187e99a1-64a5-4fdb-a18f-c6bdae1eabbd></graphic></fig> The factors that were evaluated were number and spatial distribution of ground control points (GCP). It was found that in all experiments the errors at the control points can be as high as 3.1 cm in the X and Y directions and up to 7 cm in the Z direction. The accuracy improved up to the best average errors of 1.2 cm (X, Y) and 2.1 cm (Z) for the case of random GCP placement and optimal density of ~2.5 GCP per image. The accuracy decreased with either lower number of GCPs or their biased placement along either side of the channel. Error and group analysis showed that the accuracy of the photogrammetric DSM was affected by biased GCP placement, especially when clustered toward one area. Higher resolution of the point cloud may become important for more accurate identification of rapid elevation changes in a DSM as well as channel overall geometry. Although the method and produced DSMs can be used for the estimation of the elevation changes in field-based soil erosion studies, the generated errors (especially in the vertical dimension) may be comparable to the elevation change itself. Thus, this technique must be used with caution when the expected elevation change is small.