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Unmanned aerial vehicles are used today as a standard tool to derive very high-resolution geospatial data (e.g. 3D height models, orthophotos). The workflow from images to products is very often just an easy-to-use black box processing. However, in contrast to traditional photogrammetry with very high-quality hardware and proven processing standard...
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... This is guaranteed through the optimization method used in the proposed method. Also, the accuracy of the external orientation parameters of UAV images can be very different, since the quality and precision of UAV images may differ for reasons such as being more influenced by environmental factors, sensor fluctuations, and low flight height [54]. As a result, a point's reprojection error differs depending on the UAV image. ...
To generate an orthophoto mosaic from a collection of aerial images, the original images are first orthorectified individually using a Digital Surface Model (DSM). Then, they are stitched together along some determined seamlines to form the orthophoto mosaic. Determining appropriate seamlines is a critical process, as it affects the visual and geometric quality of the results. The stitching process can usually be done in frame-to-frame or multi-frame modes. Although the latter is more efficient, both still involve a lot of pre-processing, such as creating individual orthophotos, image registration, and overlap extraction. This paper presents a novel coarse-to-fine approach that directly determines the seamline network without such pre-processing. Our method has been specifically applied for UAV photogrammetry projects where, due to the large number of images and the corresponding overlaps, the orthophoto mosaic generation can be very challenging and time-consuming. We established the seamlines simultaneously for all the images through a two-step
process. First, a DSM was generated, and a low-resolution grid was overlayed. Then, for each grid point, an optimal image was selected. Then, the grid cells are grouped into polygons based on their corresponding optimal image. Boundaries of these polygons established our seamline network. Thereafter, to generate the orthophoto mosaic, we overlayed a higher/full resolution grid on the top
of the DSM, the optimal image of each point of which was quickly identified via our low-resolution polygons. In this approach, not only seamlines were automatically generated, but also were the need for the creation, registration, and overlap extraction of individual orthophotos. Our method was systematically compared with a conventional frame-to-frame (CF) technique from different aspects, including the number of double-mapped areas, discontinuities across the seamlines network, and
the amount of processing time. The outcomes revealed a 46% decrease in orthophoto generation time and a notable reduction in the number of double-mapped areas, sawtooth effects, and object discontinuities within the constructed orthophoto mosaic.
... Therefore, testing the possible influence of the real-time detection status can effectively evaluate the problems that may arise in reality. Light changes [50], obstructions caused by aerial foreign objects [51], and motion blur [52] are three common and significant issues that can affect UAV imagery. It can be inferred from the performance of the models in the Disturb set that these three issues have varying degrees of impact on the segmentation accuracy. ...
Crop lodging is an important cause of direct economic losses and secondary disease transmission in agricultural production. Most existing methods for segmenting wheat lodging areas use a large-volume network, which poses great difficulties for annotation and crop monitoring in real time. Therefore, an ultra-lightweight model, Lodging-U2NetP (L-U2NetP), based on a novel annotation strategy which crops the images before annotating them (Crop-annotation), was proposed and applied to RGB images of wheat captured with an unmanned aerial vehicle (UAV) at a height of 30 m during the maturity stage. In the L-U2NetP, the Dual Cross-Attention (DCA) module was firstly introduced into each small U-structure effectively to address semantic gaps. Then, Crisscross Attention (CCA) was used to replace several bulky modules for a stronger feature extraction ability. Finally, the model was compared with several classic networks. The results showed that the L-U2NetP yielded an accuracy, F1 score, and IoU (Intersection over Union) for segmenting of 95.45%, 93.11%, 89.15% and 89.72%, 79.95%, 70.24% on the simple and difficult sub-sets of the dataset (CA set) obtained using the Crop-annotation strategy, respectively. Additionally, the L-U2NetP also demonstrated strong robustness in the real-time detection simulations and the dataset (AC set) obtained using the mainstream annotation strategy, which annotates images before cropping (Annotation-crop). The results indicated that L-U2NetP could effectively extract wheat lodging and the Crop-annotation strategy provided a reliable performance which is comparable with that of the mainstream one.
... A comprehensive and systematic analysis of UAV-derived product quality is presented in [4]. The results of this research indicate that accurate initial positions and the number and distribution of GCPs are among the main influencing parameters in reducing deformation in the block adjustment process. ...
... We calculated the differences between the measured coordinates (orthophoto and point cloud) and the ground coordinates, as well as the RMSE values for horizontal (RMSE xy ) and vertical accuracy (RMSE z ) for all of the check points (1) to (4). ...
... The reason might lie in the fact that the pixel size of the two flight altitudes is almost the same (approximately 1 cm), thus the geometry of image block does not differ considerably. Furthermore, the experimental results in [4], where there was investigated the impact of the flight altitude on the bundle block adjustment accuracy, showed an unexpected deviation from the theoretically calculated vertical accuracy (for 50, 60, 70 and 80 m) which decreases with altitude. However, for a particular type of UAV the accuracy increased with altitude. ...
... Increasing the overlap provides the possibility to see an object in more images which reduces the ambiguity of 3D dimensions and reinforces the triangulation block (Gruber et al. 2004). Of course, it must be noted that very high overlap shortens the aerial base and reduces the geometric accuracy (Nasrullah 2016). ...
Topographic mapping in mountainous areas encounters many challenges due to the potential impasse and lack of access to all locations. Unmanned Aerial Vehicles (UAVs) are an effective alternative to traditional field mapping in different environmental conditions. However, problems, such as large-scale differences, gaps, and errors due to extreme elevation differences in these areas, hinder the use of UAV-based photogrammetry, thus reducing the quality and accuracy of the photogrammetric products and the final extracted map in mountainous areas. By designing an optimal flight network before UAV acquisition, the effect of these problems can be reduced. This paper proposes a method for planning the dynamic three-dimensional imaging network UAV in mountainous terrain based on digital elevation model (DEM) to ensure the uniformity of the scale in the photogrammetric blocks, and avoid collision with obstacles, also gaps or data redundancy. The proposed method was implemented in a semi-mountainous area and was compared to two approaches of 3D network with static overlap and normal 2D flight plan. The results showed that the large-scale changes among the images were reduced and the ground sample distance (GSD) was maintained as constant as possible. Also, planning UAV flight program based on the proposed algorithm increases the accuracy of the photogrammetric products.
... Increasing the overlap provides the possibility to see an object in more images which reduces the ambiguity of 3D dimensions and reinforces the triangulation block [24]. Of course, it must be noted that very high overlap shortens the aerial base and reduces the geometric accuracy [25]. ...
Topographic mapping in mountainous areas encounters many challenges due to the potential impasse and lack of access to all locations. Unmanned Aerial Vehicles (UAVs) are an effective alternative to traditional field mapping in different environmental conditions. However, problems such as large-scale differences, gaps, and errors due to extreme elevation differences in these areas, hinders the use of UAV-based photogrammetry, thus reducing the quality and accuracy of the photogrammetric products and the final extracted map in mountainous areas. By designing an optimal flight network before UAV acquisition, the effect of these problems can be reduced. This paper proposes a method for planning the dynamic three-dimensional Imaging Network UAV in mountainous terrain based on digital elevation model (DEM) to ensure the uniformity of the scale in the photogrammetric blocks, avoid collision with obstacles, also gaps or data redundancy. The proposed method was implemented in a semi-mountainous area and the results showed that the large-scale changes among the images were reduced and the GSD was maintained as constant as possible. Also, planning UAV flight program based on the proposed algorithm increases the accuracy of the photogrammetric products.
... Am Beispiel der Bewegungsunschärfe (Abb. 10) findet eine Kanten und somit Mustererkennung nur entlang der Bewegungsrichtung statt, jedoch nicht senkrecht zu ihr (Nasrullah, 2016). Auch durch falsche Kameraeinstellungen können solche Effekte ausgelöst werden. ...
... Eine zu lange Belichtungszeit, zu kleine Blendenöffnung oder zu niedrige ISO-Werte haben dabei einen großen Einfluss auf die Bildqualität der Aufnahmen (Zhu, et al., 2022). (Nasrullah, 2016). Bei einer größeren Flughöhe ändert sich zudem der Abstand zum Objekt und somit der Bildausschnitt. ...
Analoge Bestandspläne und Bauwerksunterlagen sind oftmals nicht vollständig oder auf dem aktuellen Stand. Um diese Pläne zu aktualisieren, müssen die dafür notwendigen Informationen erneut erfasst werden. Bei der Umsetzung dieses Vorgangs werden heutzutage neue und alte Daten digitalisiert. Bauaufnahmen werden hauptsächlich noch mit analogen Messverfahren dokumentiert, welche zwar sehr präzise Werte liefern, allerdings in der Menge der ermittelten Werte eingeschränkt sind. Müssen Messwerte im Nachgang ergänzt werden oder sind Teile einer Erhebung vergessen worden, ist dies nur mit erheblichem zusätzlichem Aufwand zu korrigieren. Mittels digitaler Erfassungsmethoden entfallen diese Probleme, da die Modelle mittlerweile entsprechend einfacher und vollständiger erstellt werden können.
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Im Jahr 2021 ereignete sich im Ahrtal eine schwere Katastrophe durch Überschwemmung. Davon betroffen waren an der Ahr liegende Ortschaften und deren Weinanbaugebiete. In diesem Zusammenhang wurden zusätzlich viele Brückenbauwerke zerstört. Einige Bauwerke haben die Fluten überstanden dagegen zwei von ihnen (Steinbergsbrücke, Dernau; Eisenbahnbrücke, Dümpelfeld) werden im Rahmen dieser Arbeit digital erfasst und auf strukturelle Veränderungen überprüft.
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In der Arbeit soll ein Überblick gegeben werden, wie Messdaten mittels terrestrischem Laserscanning und Photogrammetrie bereits mit wenig Aufwand erhoben, analysiert und für bestehende oder neue Bestandpläne kombiniert werden können und welche Problematiken dabei zu beachten sind.
... The errors modeled in [17] included camera/lens calibration errors; motion blurriness; the altitude, pattern, and stability of flight; image overlap; and the distribution of GCPs. The main discoveries and arguments from this work include the following: ...
... Since SFM benefits from imagery from multiple perspectives, vertical accuracy decreases due to bad geometry. Examples to quantify the findings above can be found in [17]. ...
... The onboard pose error for direct geo-referencing was also considered [17]. If small UAVs can carry high-quality GNSS receivers, they may be capable of RTK on the fly or recording data for post processing. ...
UAV-based technologies are evolving and improving at a rapid pace. The abundance of solutions and systems available today can make it difficult to identify the best option for construction and civil projects. The purpose of this literature review is to examine the benefits and limitations of UAV-based sensing systems in the context of construction management and civil engineering, with a focus on camera-based and laser-based systems. The risk factors associated with UAV operations at construction sites are also considered.
... The georeferencing of the UAV images was performed using ground control points (GCPs), which were signaled with targets (a black circle 27 cm in diameter on a white background). Ideally, the GCPs should be evenly distributed over a surveyed area [27][28][29] to achieve optimal accuracy of results. The terrain of the Belca rockfall is not only steep, but it is also a challenge to reach some areas in order to set the GCPs optimally. ...
Unmanned aerial vehicle (UAV) photogrammetry is one of the most effective methods for capturing a terrain in smaller areas. Capturing a steep terrain is more complex than capturing a flat terrain. To fly a mission in steep rugged terrain, a ground control station with a terrain following mode is required, and a quality digital elevation model (DEM) of the terrain is needed. The methods and results of capturing such terrain were analyzed as part of the Belca rockfall surveys. In addition to the national digital terrain model (NDTM), two customized DEMs were developed to optimize the photogrammetric survey of the steep terrain with oblique images. Flight heights and slant distances between camera projection centers and terrain are analyzed in the article. Some issues were identified and discussed, namely the vertical images in steep slopes and the steady decrease of UAV heights above ground level (AGL) with the increase of height above take-off (ATO) at 6%-8% rate. To compensate for the latter issue, the custom DEMs and NDTM were tilted. Based on our experience, the proposed optimal method for capturing the steep terrain is a combination of vertical and oblique UAV images.
... While these served many applications well, van der Sluijs et al. [91] found the Zenmuse X5 camera onboard the DJI Inspire 1 Pro had a relatively slow-rolling shutter, producing motion distortions that had to be corrected post-flight. Increasing shutter speeds while balancing other exposure parameters such as ISO and lens aperture can improve blurred distortions [118], although specific calibrations vary with sensor, illumination, and flight plan. ...
... While these served many applications well, van der Sluijs et al. [91] found the Zenmuse X5 camera onboard the DJI Inspire 1 Pro had a relatively slow-rolling shutter, producing motion distortions that had to be corrected post-flight. Increasing shutter speeds while balancing other exposure parameters such as ISO and lens aperture can improve blurred distortions [118], although specific calibrations vary with sensor, illumination, and flight plan. In addition to the popular RGB imagers, here we suggest several new and emerging camera systems which are UAV-mountable and capable of covering entire visible, NIR, short-wave infrared (SWIR), and thermal wavelengths in hyperspectral resolutions (e.g., [51,56,76,84,101]). ...
... Experienced investigators and some multitemporal studies initially employed minimal overlap and switched to higher overlap in later flights, suggesting increased success with the more conservative approach (e.g., [38,75,83]). Additional considerations for flight planning are to keep flight orientation consistent among repeated flights to maintain shadow location and to follow parallel tracks because cross-tracks add extra flight time and have been found to be unhelpful in increasing point cloud density or accuracy if adequate control points are available [94,118]. Finally, van der Sluijs et al. [91] recommended a methodology that includes a minimum and optimized GNSS-based GCP/CP network, and if available, incorporates regional high-resolution airborne laser scanning topographic data to tie in fine-scale DSM-like products derived by UAV surveys. ...
Owing to usual logistic hardships related to field-based cryospheric research, remote sensing has played a significant role in understanding the frozen components of the Earth system. Conventional spaceborne or airborne remote sensing platforms have their own merits and limitations. Unmanned aerial vehicles (UAVs) have emerged as a viable and inexpensive option for studying the cryospheric components at unprecedented spatiotemporal resolutions. UAVs are adaptable to various cryospheric research needs in terms of providing flexibility with data acquisition windows, revisits, data/sensor types (multispectral, hyperspectral, microwave, thermal/night imaging, Light Detection and Ranging (LiDAR), and photogrammetric stereos), viewing angles, flying altitudes, and overlap dimensions. Thus, UAVs have the potential to act as a bridging remote sensing platform between spatially discrete in situ observations and spatially continuous but coarser and costlier spaceborne or conventional airborne remote sensing. In recent years, a number of studies using UAVs for cryospheric research have been published. However, a holistic review discussing the methodological advancements, hardware and software improvements, results, and future prospects of such cryospheric studies is completely missing. In the present scenario of rapidly changing global and regional climate, studying cryospheric changes using UAVs is bound to gain further momentum and future studies will benefit from a balanced review on this topic. Our review covers the most recent applications of UAVs within glaciology, snow, permafrost, and polar research to support the continued development of high-resolution investigations of cryosphere. We also analyze the UAV and sensor hardware, and data acquisition and processing software in terms of popularity for cryospheric applications and revisit the existing UAV flying regulations in cold regions of the world. The recent usage of UAVs outlined in 103 case studies provide expertise that future investigators should base decisions on.
... Številni viri opisujejo in raziskujejo različne dejavnike, ki vplivajo na kakovost izdelkov UAV fotogrametrije. Sistematično in celovito je to tematiko obdelal in predstavil Nasrullah (2016). Dejavnike, ki vplivajo na kakovost izdelkov UAV fotogrametrije, smo opisali in analizirali po naslednjih vsebinskih sklopih: ...
... Ostrino slik v fotogrametriji običajno zagotovimo s povečanjem globinske ostrine z uporabo bolj zaprte zaslonke, čas osvetlitve pa je določen z elektroniko fotoaparata. Zaradi gibanja letalnika in tresljajev, ki so prisotni predvsem pri rotorskih letalnikih, so fotografije pogosto neostre (Nasrullah, 2016;Aber et al., 2019), zato morajo biti časi osvetlitve kratki. Čas osvetlitve je običajno stalen oziroma zaklenjen; avtorji uporabljajo različne vrednosti, največkrat je predlagana 1/800 s (npr. ...
... V idealnem primeru bi morala biti tudi odprtina zaslonke stalna, saj njeno spreminjanje vpliva na distorzijo objektiva. Velike odprtine zaslonke prav tako povečajo vpliv radialne distorzije in šum na fotografiji (Nasrullah, 2016). Pri snemanju z letalnikom se običajno izbere fokusiranje na fotografsko neskončnost, s čimer zagotovimo ostrino fotografij ne glede na izbrano snemalno višino ali stopnjo razgibanosti terena. ...
Unmanned aerial vehicles, equipped with various sensors and devices, are increasingly used to acquire geospatial data in geodesy, geoinformatics, and environmental studies. In this context, a new research and professional field has been developed – UAV photogrammetry – dealing with photogrammetry data acquisition and data processing, acquired by unmanned aerial vehicles. In this study, we analyse the selected factors that impact the quality of data provided using UAV photogrammetry, with the focus on positional accuracy; they are discussed in three groups: (a) factors related to the camera properties and the quality of images; (b) factors related to the mission planning and execution; and (c) factors related to the indirect georeferencing of images using ground control points. These selected factors are analysed based on the detailed review of relevant scientific publications. Additionally, the influence of the number of ground control points and their spatial distribution on point clouds' positional accuracy has been investigated for the case study. As the conclusion, key findings and recommendations for UAV photogrammetric projects are given; we have highlighted the importance of suitable lighting and weather conditions when performing UAV missions for spatial data acquisition, quality equipment, appropriate parameters of UAV data acquisition, and a sufficient number of ground control points, which should be determined with the appropriate positional accuracy and their correct distribution in the field.
