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Landslides are geological disasters that are harmful to both humans and society. Digital elevation model (DEM) time series data are usually used to monitor dynamic changes or surface damage. To solve the problem of landslide deformation monitoring without ground control points (GCPs), a multidimensional feature-based coregistration method (MFBR) was studied to achieve accurate registration of multitemporal DEMs without GCPs and obtain landslide deformation information. The method first derives the elevation information of the DEM into image pixel information, and the feature points are extracted on the basis of the image. The initial plane position registration of the DEM is implemented. Therefore, the expected maximum algorithm is applied to calculate the stable regions that have not changed between multitemporal DEMs and to perform accurate registrations. Finally, the shape variables are calculated by constructing a DEM differential model. The method was evaluated using simulated data and data from two real landslide cases, and the experimental results revealed that the registration accuracies of the three datasets were 0.963 m, 0.368 m, and 2.459 m, which are 92%, 50%, and 24% better than the 12.189 m, 0.745 m, and 3.258 m accuracies of the iterative closest-point algorithm, respectively. Compared with the GCP-based method, the MFBR method can achieve 70% deformation acquisition capability, which indicates that the MFBR method has better applicability in the field of landslide monitoring. This study provides an idea for landslide deformation monitoring without GCPs and is helpful for further understanding the state and behavior of landslides. [ABSTRACT FROM AUTHOR]

Digital photogrammetry technology has been extensively utilized in identifying rock mass discontinuities, with its measurement accuracy and multi-source errors have been thoroughly scrutinized. However, several challenges persist in field experiments, including the issue of inaccurately measured true data and errors in geo-referencing, which hinder the reliability of results. The purpose of this paper was to overcome the limitations of traditional field experiments through the digitized process in numerical simulation of photogrammetry, which has the advantages of high efficiency and high accuracy. Based on a case study of a rock slop, both field and simulation experiments were designed to quantitatively investigate the effect of coordinate accuracy of ground control points (GCPs) on the photogrammetric accuracy of discontinuity geometric parameters. The highly consistent results between numerical simulations and field experiments further confirmed the feasibility of the numerical simulation procedure. It is promising that the numerical simulation procedure has the potential to replace the field experiments by guiding and optimizing the arrangement scheme of camera stations and GCPs before fieldwork, thereby balancing efficiency and accuracy. Highlights: A novel idea combining field and numerical experiments of photogrammetry was proposed A case study was conducted to investigate the impact of GCP coordinate accuracy. Comparison between the two methods showed the reliability of the numerical simulation method. The accuracy and efficiency of digitized workflows highlighted the superiority of numerical simulation. [ABSTRACT FROM AUTHOR]

Landslides are geological disasters that are harmful to both humans and society. Digital elevation model (DEM) time series data are usually used to monitor dynamic changes or surface damage. To solve the problem of landslide deformation monitoring without ground control points (GCPs), a multidimensional feature-based coregistration method (MFBR) was studied to achieve accurate registration of multitemporal DEMs without GCPs and obtain landslide deformation information. The method first derives the elevation information of the DEM into image pixel information, and the feature points are extracted on the basis of the image. The initial plane position registration of the DEM is implemented. Therefore, the expected maximum algorithm is applied to calculate the stable regions that have not changed between multitemporal DEMs and to perform accurate registrations. Finally, the shape variables are calculated by constructing a DEM differential model. The method was evaluated using simulated data and data from two real landslide cases, and the experimental results revealed that the registration accuracies of the three datasets were 0.963 m, 0.368 m, and 2.459 m, which are 92%, 50%, and 24% better than the 12.189 m, 0.745 m, and 3.258 m accuracies of the iterative closest-point algorithm, respectively. Compared with the GCP-based method, the MFBR method can achieve 70% deformation acquisition capability, which indicates that the MFBR method has better applicability in the field of landslide monitoring. This study provides an idea for landslide deformation monitoring without GCPs and is helpful for further understanding the state and behavior of landslides.

Copyright of Japanese Journal of JSCE / Doboku Gakkai Ronbunshu is the property of Japan Society of Civil Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We verified a method of acquiring digital surface models of steep, forested, small watershed topography, where real‐time kinematic processing is difficult because of the presence of interfering objects. Our approach involved using an uncrewed aerial vehicle with a built‐in global navigation satellite system, which reduces time and labour costs. We tested the applicability of structure‐from‐motion multi‐view stereo processing, and post‐processing motion corrections of positional coordinate data were also tested. Nine verification points were established in a small 0.5 km2 watershed, with check dams established in the headwaters of the forested area. The position accuracy and overall working time of verification points extracted by the method were compared with the position accuracy and work time obtained by a field survey using a conventional total station. The results show that the vertical error between the total station and each verification point at an altitude of 150 m ranged from 0.006 to 0.181 m. The working time of the survey was 13% of that of the total station survey. The proposed workflow shows that safe and non‐destructive topographic surveying, including fluvial geomorphological mapping, is possible with a vertical error of ±0.103 m in small watersheds. This method will be useful for rapid topographic surveying in inaccessible areas during disasters, namely, debris flow monitoring at check dam sites and efficient topographic mapping of steep valleys in forested areas where positioning ground control points is laborious. It should also be of widespread interest to geographers working with spatial challenges related to land management. [ABSTRACT FROM AUTHOR]

Remote sensing enables the measurement, extraction and presentation of useful information at various spatial and temporal scales. It is used by decision-makers to create sustainable projects. However, the high geometric accuracy of satellite images is vital for the accurate planning of sustainable projects and for accurately extracting information from remote sensing data. The geometric correction process for obtaining orthoimages requires a digital elevation model (DEM), ground control points (GCP) common in the object and image space, and a model that represents the transformation between the object space and the image space. Therefore, the accuracy of an orthoimage depends on the distribution of the ground control points, the model used, and the precision of the digital elevation model. In this study, the effect of the number and distribution of ground control points on the accuracy of the polynomial transformation model, rational function model and thin plate spline methods used in obtaining the orthoimage was investigated. The performance of the methods was evaluated by using a very high-resolution Pléiades-1B satellite image. The digital elevation model (DEM) was obtained by the photogrammetric method using aerial photographs. Experimental results demonstrate that the appropriate GCP distribution significantly improved the geometric correction accuracy of the orthoimages. [ABSTRACT FROM AUTHOR]

The “cloud control” paradigm, grounded on geocoded data such as digital orthophoto map (DOM) and digital elevation model (DEM), is gradually maturing for applications in medium and small-scale mapping. However, its essence of obtaining control points from the object space inherently carries errors within the DOM and DEM products, subsequently hindering its applicability in large-scale mapping scenarios. Constructing constraints based on original images with known accurate orientation parameters and adjusted object space tie points represents a theoretically superior cloud control paradigm. This paper proposes developing this image-based cloud control paradigm into a standardized survey and mapping product, termed digital control photo (DCP), aiming to better ensure positioning accuracy while reducing redundant labor and computational power consumption in revisit aerial survey tasks. This paper also introduces the application of DCP in calibration of large-format aerial cameras, geometric positioning of unmanned aerial vehicle images and satellite images, and suggests several recommendations for promoting the establishment of a DCP application system. [ABSTRACT FROM AUTHOR]

In environmental supervision, it is necessary to measure waste piles volume to determine whether the activities of the waste manager comply with the established requirements. The aim of this research is to determine whether the model, formed from images collected with low-priced unmanned aerial vehicle (UAV) - not with Real Time Kinematic Global Navigation Satellite System (RTK GNSS) capability - is sufficiently accurate to carry out waste-related surveying. Data collection took place in spring 2021 at the Aardlapalu transhipment station in Tartu County. The objects of the research were an unscreened composting pile and a covered composting pile. In the fieldwork, terrestrial laser scanning and photogrammetric flight were carried out. The reference value was the volume of the model formed from the data of laser scan. The volumes of all models formed by the photogrammetric method were within the permissible difference of 10% provided by law. The most accurate results were obtained from the covered composting pile with an overlap of 70% × 70% and 21 ground control points (GCPs). Using these parameters, the absolute error of the model was 1.48 m? and the relative error was 0.65%. The most inaccurate results were obtained from the unscreened composting pile with an overlap of 80% × 80% and 21 GCP-s. The research confirmed the hypothesis that sufficient accuracy to calculate waste piles volumes can also be achieved by using a cheaper UAV and camera and with software not specially designed for photogrammetry, design, and drawing. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture play a crucial role in the mapping process. Researchers are exploring solutions that use image-based techniques such as structure from motion (SfM) to produce topographic maps using UAVs while accessing locations with extremely high accuracy and minimal surface measurements. Advancements in technology have enabled real-time kinematic (RTK) to increase positional accuracy to 1–3 times the ground sampling distance (GSD). This paper focuses on post-processing kinematic (PPK) of positional accuracy to achieve a GSD or better. To achieve this, precise satellite orbits, clock information, and UAV global navigation satellite system observation files are utilized to calculate the camera positions with the highest positional accuracy. RTK/PPK analysis is conducted to improve the positional accuracies obtained from different flight patterns and altitudes. Data are collected at altitudes of 80 and 120 meters, resulting in GSD values of 1.87 cm/px and 3.12 cm/px, respectively. The evaluation of ground checkpoints using the proposed PPK methodology with one ground control point demonstrated root mean square error values of 2.3 cm (horizontal, nadiral) and 2.4 cm (vertical, nadiral) at an altitude of 80 m, and 1.4 cm (horizontal, oblique) and 3.2 cm (vertical, terrain-following) at an altitude of 120 m. These results suggest that the proposed methodology can achieve high positional accuracy for UAV image georeferencing. The main contribution of this paper is to evaluate the PPK approach to achieve high positional accuracy with unmanned aerial vehicles and assess the effect of different flight patterns and altitudes on the accuracy of the resulting topographic maps. [ABSTRACT FROM AUTHOR]

Over-exploitation of coal mines leads to surface subsidence, surface cracks, collapses, landslides, and other geological disasters. Taking a mining area in Nalintaohai Town, Ejin Horo Banner, Ordos City, Inner Mongolia Autonomous Region, as an example, Sentinel-1A data from January 2018 to October 2019 were used as the data source in this study. Based on the high interference coherence of the permanent scatterer (PS) over a long period of time, the problem of the manual selection of ground control points (GCPs) affecting the monitoring results during refinement and re-flattening is solved. A DInSAR-PS-Stacking method combining the PS three-threshold method (the coherence coefficient threshold, amplitude dispersion index threshold, and deformation velocity interval) is proposed as a means to select ground control points for refinement and re-flattening, as well as a means to obtain time-series deformation by weighted stacking processing. A SBAS-PS-InSAR method combining the PS three-threshold method to select PS points as GCPs for refinement and re-flattening is also proposed. The surface deformation results monitored by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods are analyzed and verified. The results show that the subsidence location, range, distribution, and space–time subsidence law of surface deformation results obtained by DInSAR-PS-Stacking, SBAS-PS-InSAR, and GPS methods are basically the same. The deformation results obtained by these two InSAR methods have a good correlation with the GPS monitoring results, and the MAE and RMSE are within the acceptable range. The error showed that the edge of the subsidence basin was small and that the center was large. Both methods were found to be able to effectively monitor the coal mine, but there were also shortcomings. DInSAR-PS-Stacking has a strong ability to monitor the settlement center. SBAS-PS-InSAR performed well in monitoring slow and small deformations, but its monitoring of the settlement center was insufficient. Considering the advantages of these two InSAR methods, we proposed fusing the time-series deformation results obtained using these two InSAR methods to allow for more reliable deformation results and to carry out settlement analysis. The results showed that the automatic two-threshold (deformation threshold and average coherence threshold) fusion was effective for monitoring and analysis, and the deformation monitoring results are in good agreement with the actual situation. The deformation information obtained by the comparison, and fusion of multiple methods can allow for better monitoring and analysis of the mining area surface deformation, and can also provide a scientific reference for mining subsidence control and early disaster warning. [ABSTRACT FROM AUTHOR]

Experimental studies of the accuracy of determining the plan and altitude coordinates were obtained from aerial photography materials and measured by a satellite receiver in real time. The study was conducted for aerial photography materials obtained from heights of 100 and 200 meters. In addition, the accuracy researched of using a different number of ground control points for georeferencing images - 18 and 5. Accuracy assessment was performed separately for the plain area and the area with rugged terrain. [ABSTRACT FROM AUTHOR]

Experimental studies of the accuracy of determining the plan and altitude coordinates were obtained from aerial photography materials and measured by a satellite receiver in real time. The study was conducted for aerial photography materials obtained from heights of 100 and 200 meters. In addition, the accuracy researched of using a different number of ground control points for georeferencing images – 18 and 5. Accuracy assessment was performed separately for the plain area and the area with rugged terrain.

Hovering drones use ground control points to measure the surface flow velocity of rivers. This study aims to use only GPS data and images captured by a drone to extract the flowrate at a designated absolute position. Using GPS data, the moving directions of the drone and of the image were calculated, and each image point was converted into a physical UTM (Universal Transverse Mercator) coordinate system. After determining the range of observation by selecting the start and end frames, all images of the measurement cross section were divided into reference frames (measurement subsections), and the flowrate was calculated with spatiotemporal volume obtained by gathering images for 1 sec (30 frames) for all measurement subsections. The results were comparable with those obtained using the existing hovering drone image analysis method. [ABSTRACT FROM AUTHOR]

In the rise of recent advancements in unmanned aerial vehicles, many studies have focused on using multi-modal platforms for remote inspection of industrial and construction sites. The acquisition of multiple data modalities assists the inspectors in acquiring comprehensive information about the targeted components. Despite the benefits of multi-modal platforms, the calibration and fusion of the obtained data modalities present many challenges that need to be addressed. Using a calibration board with geometrically known features to estimate intrinsic and extrinsic parameters and accurately align the images in thermal and visible spectral bands, is one of the main approaches to address the problem of dissimilarity of feature appearances in different spectrums.This study presents a comprehensive platform for drone-based multi-modal inspection of industrial and construction components, including three main components: 1) a sensor setup that can be used as a standalone system or a payload for a drone; 2) a multi-modal embedded system; and 3) a novel calibration board for multi-modal data fusion. The multi-modal embedded system provides the required features to record, transmit, and visualize the thermal, visible, and depth data synchronously. Additionally, the system presents a multi-modal fusion technique to form RGBD&T data containing thermal and texture information of the obtained 3D view. Moreover, this study introduces a novel self-heating calibration board that uses Thermoelectric Peltier modules to provide an identifiable and sharp pattern in thermal and visible images. The calibration board is designed with an aim also to be used as Ground Control Point (GCP) in drone surveys.

3D point cloud data (PCD) can accurately and efficiently capture the 3D geometric information of a target and exhibits significant potential for construction applications. Although one of the most common approaches for generating PCD is the use of unmanned aerial vehicles (UAV), UAV photogrammetry-based point clouds are erroneous. This study proposes a novel framework for automatically improving the coordinate accuracy of PCD. Image-based deep learning and PCD analysis methods are integrated into a framework that includes the following four phases: GCP (Ground Control Point) detection, GCP global coordinate extraction, transformation matrix estimation, and fine alignment. Two different experiments, as follows, were performed in the case study to validate the proposed framework: (1) experiments on the fine alignment performance of the developed framework, and (2) performance and run time comparison between the fine alignment framework and common registration algorithms such as ICP (Iterative Closest Points). The framework achieved millimeter-level accuracy for each axis. The run time was less than 30 s, which indicated the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Spatial information is essential for planning and operating a landfill; however, like quarry mapping, mapping in a landfill is a high-risk operation. Recently, the UAV photogrammetry technique has gained popularity due to its time and cost-saving potential, as well as improved safety for surveyors; however, the accuracy of UAV photogrammetric mapping depends on an appropriate number and placement pattern of ground control points (GCPs). Therefore, this study aims to constrain the optimum number and placement pattern of GCPs in risk areas using a case study of two landfills with different characteristics. The accuracy of this mapping was computed in terms of pixels (root mean square error/ground sampling distance: RMSE/GSD) and classified under the ASPRS 2014 standard. The optimum number of GCPs was found to be '0, with a GCP density of roughly 143-166 GCPs per km². The optimum GCP placement pattern has 70-80% of the GCPs at the boundary and 20-30% in the center of the area, which greatly reduces the risk of placing GCPs, especially in the center of the survey area. The horizontal accuracy (RMSEX/GSD, RMSEY/GSD) of this configuration was less than one pixel; thus, this orthoimage could be used in high-accuracy work. The vertical accuracy (RMSEZ/GSD) of the 3D model qualifies for the 10-cm vertical accuracy class. Therefore, these GCP placement guidelines can be applied to other mapping activities in risk areas, such as flood-prone areas, slope mapping, and geohazard studies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Over-exploitation of coal mines leads to surface subsidence, surface cracks, collapses, landslides, and other geological disasters. Taking a mining area in Nalintaohai Town, Ejin Horo Banner, Ordos City, Inner Mongolia Autonomous Region, as an example, Sentinel-1A data from January 2018 to October 2019 were used as the data source in this study. Based on the high interference coherence of the permanent scatterer (PS) over a long period of time, the problem of the manual selection of ground control points (GCPs) affecting the monitoring results during refinement and re-flattening is solved. A DInSAR-PS-Stacking method combining the PS three-threshold method (the coherence coefficient threshold, amplitude dispersion index threshold, and deformation velocity interval) is proposed as a means to select ground control points for refinement and re-flattening, as well as a means to obtain time-series deformation by weighted stacking processing. A SBAS-PS-InSAR method combining the PS three-threshold method to select PS points as GCPs for refinement and re-flattening is also proposed. The surface deformation results monitored by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods are analyzed and verified. The results show that the subsidence location, range, distribution, and space–time subsidence law of surface deformation results obtained by DInSAR-PS-Stacking, SBAS-PS-InSAR, and GPS methods are basically the same. The deformation results obtained by these two InSAR methods have a good correlation with the GPS monitoring results, and the MAE and RMSE are within the acceptable range. The error showed that the edge of the subsidence basin was small and that the center was large. Both methods were found to be able to effectively monitor the coal mine, but there were also shortcomings. DInSAR-PS-Stacking has a strong ability to monitor the settlement center. SBAS-PS-InSAR performed well in monitoring slow and small deformations, but its monitoring of the settlement center was insufficient. Considering the advantages of these two InSAR methods, we proposed fusing the time-series deformation results obtained using these two InSAR methods to allow for more reliable deformation results and to carry out settlement analysis. The results showed that the automatic two-threshold (deformation threshold and average coherence threshold) fusion was effective for monitoring and analysis, and the deformation monitoring results are in good agreement with the actual situation. The deformation information obtained by the comparison, and fusion of multiple methods can allow for better monitoring and analysis of the mining area surface deformation, and can also provide a scientific reference for mining subsidence control and early disaster warning.

Image-based unmanned aircraft systems (UASs) are used in a variety of geodetic applications. Precise 3D terrain surface mapping requires ground control points (GCPs) for scaling and (indirect) georeferencing. In image analysis software (e.g., Agisoft Metashape), the images can be generated to a 3D point cloud using Structure-from-Motion (SfM). In general, the conventional GCP design for UAS flights is a checkerboard pattern, which is provided in the software and used for automatic marker detection in each image. When changing the pattern, manual work would be required by picking the GCP individually by hand. To increase the level of automation in the evaluation, this article aims to present a workflow that automatically detects a new edge-based GCP design pattern in the images, calculates their center points, and provides this information to the SfM software. Using the proposed workflow based on deep learning (DL) and image processing, the quality of the resulting 3D model can be equated to the result with GCP center points picked by human evaluator. Consequently, the workload can be accelerated with this approach.

3D point cloud data (PCD) can accurately and efficiently capture the 3D geometric information of a target and exhibits significant potential for construction applications. Although one of the most common approaches for generating PCD is the use of unmanned aerial vehicles (UAV), UAV photogrammetry-based point clouds are erroneous. This study proposes a novel framework for automatically improving the coordinate accuracy of PCD. Image-based deep learning and PCD analysis methods are integrated into a framework that includes the following four phases: GCP (Ground Control Point) detection, GCP global coordinate extraction, transformation matrix estimation, and fine alignment. Two different experiments, as follows, were performed in the case study to validate the proposed framework: (1) experiments on the fine alignment performance of the developed framework, and (2) performance and run time comparison between the fine alignment framework and common registration algorithms such as ICP (Iterative Closest Points). The framework achieved millimeter-level accuracy for each axis. The run time was less than 30 s, which indicated the feasibility of the proposed framework.

The paper presents an application of the methodology used in the paper "Synthetic analysis of geoinformatics technologies for cultural heritage conservation, methodological approach". The creation of the 3D model of the Rákóczi-Bánffy Castle in Urmeniș, Bistrița Năsăud County, was done by applying and integrating Terrestrial Laser Scanning (TLS) technologies and aerial photogrammetry performed with an Unmanned Aerial Vehicle (UAV). Agisoft Photoscan was used to compare the results and then they were compared with the images scanned using CloudCompare software. Thus, following the performance, with the help of the mentioned software, of a series of processing of the point cloud obtained, through the two imaging technologies, the error between the points belonging to the point cloud taken with UAV and the one taken with Laser Scanner was between 1 and 15 mm, the margin of error being acceptable for monuments without complex architectural details, so that the point cloud resulting from UAVs can be used successfully in this activity. The aim of the paper is to elaborate a geomatic methodology with an optimized cost-quality ratio, later replicable in the analysis of the current state of other constructions of the same type, knowing that over 600 castles in Transylvania alone are in a similar state, and such cases can be found in other Central European states as well. [ABSTRACT FROM AUTHOR]

SRP (Space Reference Points) is a worldwide, accurate, dense and homogeneous 3D GCP database, built from a plethoric SPOT6/7 multi-view archive. The project, carried out as a partnership between « Institut national de l'information géographique et forestière » (IGN) and Airbus Defence and Space (ADS), aims at automatically locating high-resolution imagery with a 3 m accuracy, all around the world. SRP in Africa was produced in 2019. Quality controls confirm compliance with the expected specification for this product. The particularities of the landscapes (including desert and dense forest) and the climatic variations encountered on this continent have led to the integration of new functionalities into the production chain. First, SPOT6/7 image selection was enhanced in the intertropical zone by taking into account the cloud masks supplied with these images. This allows for optimal SRP density in this region. In addition, a geometric foundation prototype including Sentinel-2 ortho-images was developed in order to fulfil the 3 m location specification on groups of islands (in this case, the Cape Verde islands). Then, to entirely validate the product, the article presents two operational tests on Nigeria for 2D production and on the city of Marrakech for 3D production. These tests prove SRP capacity to locate different types of images and to reach the target location accuracy of the database. As from 2021, SRP is intended to ensure the registration of images in various projects and, especially, on a large scale, in the Pléiades Neo ground segment (30 cm resolution). [ABSTRACT FROM AUTHOR]

Usually, the rational polynomial coefficient (RPC) model of spaceborne synthetic aperture radar (SAR) is fitted by the original range Doppler (RD) model. However, the radar signal is affected by two-way atmospheric delay, which causes measurement error in the slant range term of the RD model. In this paper, two atmospheric delay correction methods are proposed for use in terrain-independent RPC fitting: single-scene SAR imaging with a unique atmospheric delay correction parameter (plan 1) and single-scene SAR imaging with spatially varying atmospheric delay correction parameters (plan 2). The feasibility of the two methods was verified by conducting fitting experiments and geometric positioning accuracy verification of the RPC model. The experiments for the GF-3 satellite were performed by using global meteorological data, a global digital elevation model, and ground control data from several regions in China. The experimental results show that it is feasible to use plan 1 or plan 2 to correct the atmospheric delay error, no matter whether in plain, mountainous, or plateau areas. Moreover, the geometric positioning accuracy of the RPC model after correcting the atmospheric delay was improved to better than 3 m. This is of great significance for the efficient and high-precision geometric processing of spaceborne SAR images. [ABSTRACT FROM AUTHOR]

Positioning the pixels of ground control points (GCPs) in drone images is an issue of great concern in the field of drone photogrammetry. The current mainstream automatic approaches are based on standardized markers, such as circular coded targets and point coded targets. There is no denying that introducing standardized markers improves the efficiency of positioning GCP pixels. However, the low flexibility leads to some drawbacks, such as the heavy logistical input in placing and maintaining GCP markers. Especially as drone photogrammetry steps into the era of large scenes, the logistical input in maintaining GCP markers becomes much more costly. This paper proposes a novel positioning method applicable for non-standardized GCPs. Firstly, regions of interest (ROIs) are extracted from drone images with stereovision technologies. Secondly, the quality of ROIs is evaluated using image entropy, and then the outliers are filtered by an adjusted boxplot. Thirdly, pixels of interest are searched with a corner detector, and the precise imagery coordinates are obtained by subpixel optimization. Finally, the verification was carried out in an urban scene, and the results show that this method has good applicability to the GCPs on road traffic signs, and the accuracy rate is over 95%.

Digital terrain model (DTM) generation is essential to recreating terrain morphology once the external elements are removed. Traditional survey methods are still used to collect accurate geographic data on the land surface. Given the emergence of unmanned aerial vehicles (UAVs) equipped with low-cost digital cameras and better photogrammetric methods for digital mapping, efficient approaches are necessary to allow rapid land surveys with high accuracy. This paper provides a review, complemented with the authors’ experience, regarding the UAV photogrammetric process and field survey parameters for DTM generation using popular commercial photogrammetric software to process images obtained with fixed-wing or multicopter UAVs. We analyzed the quality and accuracy of the DTMs based on four categories: (i) the UAV system (UAV platforms and camera); (ii) flight planning and image acquisition (flight altitude, image overlap, UAV speed, orientation of the flight line, camera configuration, and georeferencing); (iii) photogrammetric DTM generation (software, image alignment, dense point cloud generation, and ground filtering); (iv) geomorphology and land use/cover. For flat terrain, UAV photogrammetry provided a horizontal root mean square error (RMSE) between 1 to 3 × the ground sample distance (GSD) and a vertical RMSE between 1 to 4.5 × GSD, and, for complex topography, a horizontal RMSE between 1 to 7 × GSD and a vertical RMSE between 1.5 to 5 × GSD. Finally, we stress that UAV photogrammetry can provide DTMs with high accuracy when the photogrammetric process variables are optimized.

Shin, B.-S. and Kim, K.-H., 2023. Dynamic observation for terrain volume estimation using UAV-based photogrammetry. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 658-662. Charlotte (North Carolina), ISSN 0749-0208. Shoreline changes its shapes and attribution dynamically through natural, and unnatural acts and is the most information for the country. These shorelines can apply to the framework data of MGIS (Marine Geographic Information System), and they are getting important to implement a phase of monitoring around coastal areas. Recently, low-cost, miniaturized hyperspectral imaging technology is becoming available for small Unmanned Aerial Vehicle (UAV) platforms. Shoreline change has been measured with conventional surveying techniques such as Total station, GNSS, EDM, etc. These measurements provide short and long-term variations of nearshore evolution which enables us to estimate the erosional and accretion sediment volume of the beach. This observation of coastal morphology currently has been utilized through the advance of optical imaging systems and related digital image analysis. The purpose of this study is to examine the accuracy according to the number and location of ground reference points prior to shoreline survey using unmanned aerial vehicles. Therefore, in this study, in order to analyze the accuracy of topographical information according to the distribution of ground reference points, the topographical information was analyzed using image data captured using an unmanned aerial vehicle and the topography was analyzed using ground-type LiDAR (Light Detection And Ranging) Information was compared with each other. This study was conducted using more than 2,000 photos and 60 ground points taken in a total area of 900 ha, and it was confirmed that the accuracy of geographic information greatly depends on the number of GCPs used for Bundle Adjustment (BA). [ABSTRACT FROM AUTHOR]

Currently, products that are obtained by Unmanned Aerial Systems (UAS) image processing based on structure-from-motion photogrammetry (SfM) are being investigated for use in high precision projects. Independent of the georeferencing process being done directly or indirectly, Ground Control Points (GCPs) are needed to increase the accuracy of the obtained products. A minimum of three GCPs is required to bring the results into a desired coordinate system through the indirect georeferencing process, but it is well known that increasing the number of GCPs will lead to a higher accuracy of the final results. The aim of this study is to find the suitable number of GCPs to derive high precision results and what is the effect of GCPs systematic or stratified random distribution on the accuracy of the georeferencing process and the final products, respectively. The case study involves an urban area of about 1 ha that was photographed with a low-cost UAS, namely, the DJI Phantom 3 Standard, at 28 m above ground. The camera was oriented in a nadiral position and 300 points were measured using a total station in a local coordinate system. The UAS images were processed using the 3DF Zephyr software performing a full BBA with a variable number of GCPs i.e., from four up to 150, while the number and the spatial location of check points (ChPs) was kept constant i.e., 150 for each independent distribution. In addition, the systematic and stratified random distribution of GCPs and ChPs spatial positions was analysed. Furthermore, the point clouds and the mesh surfaces that were automatically derived were compared with a terrestrial laser scanner (TLS) point cloud while also considering three test areas: two inside the area defined by GCPs and one outside the area. The results expressed a clear overview of the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results. The RMSE can be reduced down to 50% when switching from four to 20 GCPs, whereas a higher number of GCPs only slightly improves the results.