Performance Analysis of a Drone Development Kit-derived Digital Elevation Model

In this study, we generated Digital Elevation Model (DEM) from Unmanned Aerial Vehicle (UAV) to confirm the suitability of UAV to the tidal flat study. The DEMs were generated from aerial triangulation method using rotary-wing UAV. For the accurate generation of mosaic images and DEM, the distorted images occurred by interior and exterior orientation were corrected using camera calibration. In addition, we set up a Ground Control Points (GCPs) in order to correct of the UAV position error. Therefore, the DEM was obtained with geometric error less than 30 cm. The height of generated DEM by UAV was compared with the levelled elevation by RTK-GPS and TanDEM-X radar satellite DEM. From this study, we could confirm that accurate DEM of the tidal flat can be generated using UAV and these detailed spatial information about tidal flat will be widely used for tidal flat management.

The volcanic Trindade Island is a remote Brazilian offshore territory in the South Atlantic, located ca. 1.140 kilometers east of the southeast coast of Brazil. The island’s permanent exposure to geological hazards requires assessment. However, the lack of erosion and landslides temporal data impedes predictive geohazard analyses. Therefore, we compiled pre-existing data from nautical charts and surveyed the surface terrain on Trindade Island to generate Digital Terrain Models (DTMs) and comparative accuracy analyses. The DTM based on pre-existing data shows the lowest accuracy (root mean square error - RMSE: 12.3 m) yet is adequate for regional studies. In contrast, the DTM developed from real-time kinematic global navigation satellite systems (RTK-GNSS) has the highest vertical accuracy (RMSE: 0.48 m), but spatial variability of ground elements was underestimated and limited to meter-sized (and larger) elements. The DTM obtained using the unmanned aerial vehicle (UAV) with ground control points (GCP), on the other hand, presented lower accuracy (RMSE: 2.37 m) than the RTK-GNSS model but still allowed observation of centimetric (and larger) ground features. For geohazard assessment on Trindade Island, models that allow fine-scale studies are needed. A UAV with GCP provides such standards and proved to be the most viable option in remote and complex sites as well. Hence, this study, the first to allow multi-temporal analysis of geohazard assessment on Trindade Island, offers a viable solution for similar analyses in other remote locations.

A comparative evaluation of digital elevation models (DEMs) for the cartographic support of adaptive landscape agriculture at the microzonal level was conducted. The study was carried out in 2025 within a working plot on a slope agro-landscape of an experimental field in the Belgorod region (plot area 8.4 ha). The aim was to assess data sources of DEMs for creating an accurate cartographic basis for designing adaptive landscape agriculture on slope agro-landscapes. Methods involved comparing global satellite models (SRTM1 v3, AW3D30, FABDEM with 30 m resolution) with a high-precision DEM obtained from unmanned aerial vehicle (UAV) aerial photography data with an accuracy of up to 3 cm/pixel. Slope and flow direction maps were constructed and analyzed, with verification performed using agrochemical soil analysis data for available phosphorus content. It was established that satellite DEMs show elevation intervals of 2 to 5 m and significant relief generalization, completely smoothing out microforms (rills, gullies), which makes correct hydrological modeling impossible. The UAV-based DEM accurately represents microrelief, enabled the construction of a flow direction map, and identified a predominant western direction. The spatial coincidence of zones with minimum available phosphorus content with washout zones defined by the high-precision DEM confirmed its practical value. For the cartographic support of microzonal-level adaptive landscape agriculture projects, especially on erosion-hazardous slopes, it is necessary to use high-precision DEMs created from UAV aerial photography data. This allows for the delineation of ecological landscape microzones, substantiated placement of anti-erosion elements, and development of differentiated agro-technologies.

Unmanned aerial vehicle (UAV)-based snow depth is mapped as the difference between snow-on and snow-off digital surface models (DSMs), which are derived using the structure from motion (SfM) technique with ground control points (GCPs). In this study, we evaluated the impacts of the quality and deployment of GCPs on the accuracy of snow depth estimates. For 15 GCPs in our study area, we surveyed each of their coordinates using an ordinary global positioning system (GPS) and a differential GPS, producing two sets of GCP measurements (hereinafter, the low-accuracy and high-accuracy sets). The two sets of GCP measurements were then incorporated into SfM processing of UAV images by following two deployment strategies to create snow-off and snow-on DSMs and then to retrieve snow depth. In Strategy A, the same GCP measurements in each set were used to create both the snow-on and snow-off DSMs. In Strategy B, each set of GCP measurements was divided into two sub-groups, one sub-group for creating snow-on DSMs and the other sub-group for snow-off DSMs. The accuracy of snow depth estimates was evaluated in comparison to concurrent in-situ snow depth measurements. The results showed that Strategy A, using both the low-accuracy and high-accuracy sets, generated accurate snow depth estimates, while in Strategy B, only the high-accuracy set could generate reliable snow depth estimates. The results demonstrated that the deployment of GCPs had a significant influence on UAV-based SfM snow depth retrieval. When accurate GCP measurements cannot be guaranteed (e.g., in mountainous regions), Strategy A is the optimal option for producing reliable snow depth estimates. When highly accurate GCP measurements are available (e.g., collected by differential GPS in open space), both deployment strategies can produce accurate snow depth estimates.

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

Abstract. This paper looks into the latest achievement in the low-cost Unmanned Aerial Vehicle (UAV) technology in their capacity to map the semi-development areas. The objectives of this study are to establish a new methodology or a new algorithm in image registration during interior orientation process and to determine the accuracy of the photogrammetric products by using UAV images. Recently, UAV technology has been used in several applications such as mapping, agriculture and surveillance. The aim of this study is to scrutinize the usage of UAV to map the semi-development areas. The performance of the low cost UAV mapping study was established on a study area with two image processing methods so that the results could be comparable. A non-metric camera was attached at the bottom of UAV and it was used to capture images at both sites after it went through several calibration steps. Calibration processes were carried out to determine focal length, principal distance, radial lens distortion, tangential lens distortion and affinity. A new method in image registration for a non-metric camera is discussed in this paper as a part of new methodology of this study. This method used the UAV Global Positioning System (GPS) onboard to register the UAV image for interior orientation process. Check points were established randomly at both sites using rapid static Global Positioning System. Ground control points are used for exterior orientation process, and check point is used for accuracy assessment of photogrammetric product. All acquired images were processed in a photogrammetric software. Two methods of image registration were applied in this study, namely, GPS onboard registration and ground control point registration. Both registrations were processed by using photogrammetric software and the result is discussed. Two results were produced in this study, which are the digital orthophoto and the digital terrain model. These results were analyzed by using the root mean square errors and mean absolute error to determine the level of accuracy and the precision of photogrammetric products. It can be concluded that the new method of image registration by using the GPS onboard of the UAV produces medium accuracy result compared to the method that uses the ground control point. This new method can be used for the medium accuracy requirements. Unmanned Aerial Vehicle can be used for several applications, which requires a medium accuracy.

This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades and pavements and foliage such as grass and trees. This involves using both unmanned aerial vehicles (UAVs) which provide high-resolution mosaic Orthoimages and generate a Digital Surface Model (DSM). For the study area chosen for this paper, 400 Orthoimages with a spatial resolution of 7 cm each were used to build the Orthoimages and DSM, which were georeferenced using well distributed network of ground control points (GCPs) of 12 reference points (RMSE = 8 cm). As these were combined with onboard RTK-GNSS-enabled 2-frequency receivers, they were able to provide absolute block orientation which had a similar accuracy range if the data had been collected by traditional indirect sensor orientation. Traditional indirect sensor orientation involves the GNSS receiver in the UAV receiving a differential signal from the base station through a communication link. This allows for the precise position of the UAV to be established, as the RTK uses correction, allowing position, velocity, altitude and heading to tracked, as well as the measurement of raw sensor data. By assessing the results of the confusion matrices, it can be seen that the overall accuracy of the object-oriented classification was 84.37%. This has an overall Kappa of 0.74 and the data that had poor classification accuracy included shade, parking lots and concrete pavements. These had a producer accuracy (precision) of 81%, 74% and 74% respectively, while lakes and solar panels each scored 100% in comparison, meaning that they had good classification accuracy.

Unmanned aerial vehicle (UAV) photogrammetry is a technology used to create highly accurate maps, and the real-time kinematic (RTK) global navigation satellite system (GNSS) network has been implemented by several organizations to support their missions.Recent advancements in computer vision have driven progress in photogrammetry, alongside the development of more accessible UAVs equipped with GNSS technology.This paper evaluates the accuracy of UAV imagery and processing using ground control points (GCPs) with coordinates from the Thailand RTK GNSS network.The study area is located on the Sathing-Phra Peninsula in Songkhla Province, southern Thailand, covering approximately 174 hectares with a total of 660 images.The average ground sampling distance (GSD) was around 4.35 cm.The processed photogrammetry system used 8 GCPs via the GNSS RTK network, and results were compared with 25 checkpoints from a static survey.The UAV photogrammetry results showed a horizontal accuracy for orthomosaics and vertical accuracy for the digital surface model (DSM), with CE 95 values of 6.99 cm and 12.44 cm, respectively, as per the NSSDA standard.These results comply with the Thailand UAV Surveying for Engineering (TUSE) standards.According to the 2014 American Society for Photogrammetry and Remote Sensing (ASPRS) standards, horizontal accuracy class I and vertical accuracy class II should be applied.The map scale factor (MSF) standard was 1:160, with a contour interval of 20 cm.Additionally, the orthomosaic DSM can be imported into open-source programs for use in a web GIS online system, enabling local agencies to retrieve and utilize the data.

Recent technological innovations have led to the available of lightweight Unmanned Aerial Vehicle (UAV) and Structure-from-Motion (SfM) photogrammetry that are successfully applied for 3D topographic surveys. However, application of UAV and SfM for complex topographic areas i.e. open-pit mine areas is still poorly understood. This paper aims to investigate and verify potential application of these techniques for generating Digital Surface Model (DSM) at open-pit coal mine area and assessing its accuracy. For this purpose, the Nui Beo open-pit coal mine located in northeast Vietnam is selected as a case study. Accordingly, a total of 206 photos were captured using DJI Phantom 3 Professional. In addition, 19 ground control points (GCPs) were established using Leica TS09 total station. The accuracy of DSM was assessed using root-mean-square error (RMSE) in X, Y, Z, XY, and XYZ components. The result showed that the DSM model has high accuracy, RMSE on the 12 calibrated GCPs for X, Y, Z, XY, XYZ is 1.1 cm, 1.9 cm, 0.8 cm, 2.2 cm, and 2.3 cm, respectively, whereas RMSE on the 7 checked GCPs is 1.8 cm, 2.4 cm, 3.2 cm, 3.0 cm, and 4.4 cm for X, Y, Z, XY, XYZ components, respectively. We concluded that small UAV and SfM are feasible and valid tools for 3D topographic mapping in complex terrains such as open-pit coal mine areas.

This study is about systematic approach to photogrammetric survey that is applicable in the extraction of elevation data for geophysical surveys in hilly terrains using Unmanned Aerial Vehicles (UAVs). The outcome will be to acquire high-quality geophysical data from areas where elevations vary by locating the best survey lines. The study area is located at the proposed construction site for the development of a water reservoir and related infrastructure in Kampus Pauh Putra, Universiti Malaysia Perlis. Seismic refraction surveys were carried out for the modelling of the subsurface for detailed site investigations. Study were carried out to identify the accuracy of the digital elevation model (DEM) produced from an UAV. At 100 m altitude (flying height), over 135 overlapping images were acquired using a DJI Phantom 3 quadcopter. All acquired images were processed for automatic 3D photo-reconstruction using Agisoft PhotoScan digital photogrammetric software, which was applied to all photogrammetric stages. The products generated included a 3D model, dense point cloud, mesh surface, digital orthophoto, and DEM. In validating the accuracy of the produced DEM, the coordinates of the selected ground control point (GCP) of the survey line in the imaging area were extracted from the generated DEM with the aid of Global Mapper software. These coordinates were compared with the GCPs obtained using a real-time kinematic global positioning system. The maximum percentage of difference between GCP’s and photogrammetry survey is 13.3 %. UAVs are suitable for acquiring elevation data for geophysical surveys which can save time and cost.

Recent advances in structure from motion (SfM) and dense matching algorithms enable surface reconstruction from unmanned aerial vehicle (UAV) images with high spatial resolution, allowing for new insights into earth surface processes. However, accuracy issues are inherent in parallel‐axes UAV image configurations. In this study, the quality of digital elevation models (DEMs) is assessed using images from a simulated UAV flight. Five different SfM tools and three different cameras are compared. If ground control points (GCPs) are not integrated into the adjustment process with parallel‐axes image configurations, significant dome‐effect systematic errors are observed, which can be reduced based on calibration parameters retrieved from a testfield captured with convergent images immediately before or after the UAV flight. A comparison between DEMs of a soil surface generated from UAV images and terrestrial laser‐scanning data show that natural surfaces can be very accurately reconstructed from UAV images, even when GCPs are missing and simple geometric camera models are considered.

The planimetric and altimetric accuracies of the Digital Elevation Model (DEM) obtained from Unmanned Aerial Vehicle (UAV) depend on some photogrammetric parameters such as flight height, flight speed, and/or ground sample distance. In the last few years, the study of computer vision algorithms has increased focusing on their importance on the photogrammetric reconstruction process for land surface mapping. According to this, the main goal of this paper was to evaluate the performance of different photogrammetric processing software, such as Open Drone Map (ODM), Agisoft PhotoScan, and Pix4D, on DEM accuracy. For this purpose, a DJI Phantom 4 Pro drone was used for the acquisition of 600 images in a difficult topography area (630 ha) with 27 Ground Control Points (GCP) previously established. The photogrammetric products were generated, and a statistical analysis was carried out for the comparison of the DEMs. The results of the Root Mean Square Error (RMSE) show that the planimetric and altimetric accuracy of Agisoft PhotoScan (RMSExy = 0.514 m; RMSEz = 0.162 m) is greater than those obtained using Pix4D and ODM. Unsatisfactory results were obtained with ODM since deformations and high planimetric and altimetric errors were identified in the orthomosaic and DEM, which was related to inefficient key point detection and an incomplete mosaic construction process. Agisoft PhotoScan modeled the anthropogenic objects as part of the land surface, while Pix4d smoothed the terrain, eliminating these anthropogenic objects and not considering them as part of the ground surface.

The Digital Elevation Model (DEM) contains elevation and slope information, making interpretation easierso that it is a fundamental element of geospatial data and can be used as a mapping source. The use oftechnology in the field of photogrammetric mapping is demonstrated by the use of Unmanned Aerial Vehicle(UAV) or drones in mapping and Geodetic GPS tied to benchmarks to obtain Ground Control Point (GCP)coordinates as ground control points. This research aims to make the Palangka Raya University DEM usingUAV and Geodetic GPS. In processing aerial photo data, the photogrammetric method is used. The time fortaking aerial photo data begins on 14-21 October 2022 which is divided into 10 flying missions with a flightaltitude of 100 m, overlap and sidelap of 75%, and camera angle of 90 with a total of 7,935 photos. GCP dataretrieval using the Real Time Kinematic (RTK) method with Geodetic GPS was carried out on October 14,2022 with a total of 33 coordinate points. The DEM that has been formed can be seen that mapping usingUAVs/drones and Geodetic GPS produces high-resolution photo data that can be used for making DigitalElevation Models (DEMs) with the highest altitude at Palangka Raya University, namely 90 meters and thelowest altitude at 50 meters. The DEM resolution obtained is 18.1 cm/pixel and the interpolation point densityis 30.7 points/m2.

Landslide is usually happening anywhere in Malaysia without any warning. Most of landslide occurs at manmade slope and natural slopes according to their slope gradient. Recently, Unmanned Aerial Vehicle (UAV) has been widely used for mapping purpose. The first objective of this study is to assess capabilities of UAV in the production of digital photogrammetric products based on landslide risk area along Jeli-Gerik highway. The study area covered is along Jeli-Gerik highway and its surrounding area. Primary data of ground control points (GCP) and check points (CP) were established using real time kinematic technique of Global Positioning System (GPS). All the aerial photographs were processed using digital photogrammetric software and the output in the form of digital elevation model (DEM) and orthophoto were produced. The second objective is to produce landslide risk area map using UAV technology. The risk area map separates the landslide area into three groups which are Low Risk, Medium Risk and High Risk. The parameters that were used to generate the landslide risk area map are slope, aspect and elevation. GIS based Landslide Hazard Zonation models helps not only to map and monitor landslides but also to predict future slope failures. In addition, data collected by UAV can be used to produce accurate ground surface surveys. As conclusion, UAV system has potential use for large scale mapping and could be used by public work department in order to monitoring of landslide area with low cost, less manpower and faster. The ability to detect landslide scarps will lead to a better understanding of landslide mechanisms for the propose area, thus leading to an enhanced identification of the most likely failure sites within a landslide-prone area.

Combined with the structure from motion (SfM) technique, unmanned aerial vehicles (UAVs) are powerful tools for generating high-resolution digital elevation models (DEMs) for application in hazard assessments. During our field observations in October 2015 at Langtang Village, which was destroyed by the Gorkha earthquake in April 2015, three different UAVs with mounted cameras were operated to evaluate the volume of the avalanche deposit covering the village. This study evaluated the performance of DEMs created from the different cameras on board those UAVs. Multiple DEMs for the different cameras, including Sony-α7R (PA7), Ricoh-GR (XGR), and Canon-IXUS (EIX), were created using SfM software. All DEMs were compared with a base DEM created from differential global positioning system survey data, which was obtained simultaneously with the UAV campaigns. The results show that the elevation difference of PA7-, XGR-, and EIX-DEMs are within ±0.14 m; the standard deviations of elevation difference range from 0.33 to 0.40 m. Although there were slightly larger differences in elevation on the southwest-to-west sides of the XGR- and EIX-DEMs, which can be attributed mainly to the flight paths and ground control point network, our DEMs are still of high enough quality to be used in hazard assessments.