scholarly journals A Study on 3D Model Construction by Controlling Unmanned Aerial Vehicle of Camera Angle

2021 ◽  
Vol 21 (2) ◽  
pp. 149-157
Author(s):  
Youngseok Song ◽  
Hyeong Jun Lee ◽  
Byung Sik Kim ◽  
Moojong Park
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
3D Model ◽  
3D Models ◽  
Aerial Photographs ◽  
Camera Angle ◽  
Aerial Vehicle ◽  
2D And 3D ◽  
Quantitative Impact ◽  
Orthophoto Images

In this study, the camera angle of a UAV (Unmanned Aerial Vehicle) was adjusted to take aerial photographs, and 2D and 3D models were constructed to evaluate the quantitative impact. The study area was Waryong Bridge, located in Dalseong-gun, Daegu Metropolitan City. The camera angles of the UAV were 90°, 75°, and 60°; DSM, orthophoto, and the 3D model were analyzed. As a result of the analysis of DSM and orthophoto, images were 1.05 times in 75° and 1.10 times in 60° compared to 90°, and matching was 1.09 times in 75° and 1.60 times in 60° compared to 90°. The point cloud for building 3D models increased analysis points to 1.17 times for 75° and 1.47 times for 60° compared to 90°. However, the area of an orthophoto of 1 pixel increased by 1.10 times for 75° and 1.34 times for 60° compared to 90°, resulting in a decrease in resolution. As the camera angle of the UAV decreases, the overlapping ratio of aerial photographs increases and the structure of wide areas can be implemented. However, since a large difference occurs in the precision of the 3D model, a shooting plan according to the purpose should be established.

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals VERIFICATION OF POTENCY OF AERIAL DIGITAL OBLIQUE CAMERAS FOR AERIAL PHOTOGRAMMETRY IN JAPAN

2016 ◽  
Vol XLI-B1 ◽  
pp. 63-68 ◽  
Author(s):  
Ryuji Nakada ◽  
Masanori Takigawa ◽  
Tomowo Ohga ◽  
Noritsuna Fujii
Keyword(s):  
3D Model ◽  
3D Models ◽  
Aerial Photographs ◽  
Digital Cameras ◽  
Digital Mapping ◽  
Aerial Photogrammetry ◽  
Public Survey ◽  
Survey Accuracy ◽  
Left And Right ◽  
Simultaneous Adjustment

Digital oblique aerial camera (hereinafter called “oblique cameras”) is an assembly of medium format digital cameras capable of shooting digital aerial photographs in five directions i.e. nadir view and oblique views (forward and backward, left and right views) simultaneously and it is used for shooting digital aerial photographs efficiently for generating 3D models in a wide area. <br><br> For aerial photogrammetry of public survey in Japan, it is required to use large format cameras, like DMC and UltraCam series, to ensure aerial photogrammetric accuracy. <br><br> Although oblique cameras are intended to generate 3D models, digital aerial photographs in 5 directions taken with them should not be limited to 3D model production but they may also be allowed for digital mapping and photomaps of required public survey accuracy in Japan. <br><br> In order to verify the potency of using oblique cameras for aerial photogrammetry (simultaneous adjustment, digital mapping and photomaps), (1) a viewer was developed to interpret digital aerial photographs taken with oblique cameras, (2) digital aerial photographs were shot with an oblique camera owned by us, a Penta DigiCAM of IGI mbH, and (3) accuracy of 3D measurements was verified.

scholarly journals Automated Geolocation in Urban Environments Using a Simple Camera-Equipped Unmanned Aerial Vehicle: A Rapid Mapping Surveying Alternative?

2020 ◽  
Vol 9 (7) ◽  
pp. 425
Author(s):  
Dimitrios Trigkakis ◽  
George Petrakis ◽  
Achilleas Tripolitsiotis ◽  
Panagiotis Partsinevelos
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Local Coordinate System ◽  
Arbitrary Point ◽  
Urban Environments ◽  
Signal Interference ◽  
Rapid Mapping ◽  
Gnss Positioning ◽  
Area Of Interest ◽  
Aerial Vehicle

GNSS positioning accuracy can be degraded in areas where the surrounding object geometry and morphology interacts with the GNSS signals. Specifically, urban environments pose challenges to precise GNSS positioning because of signal interference or interruptions. Also, non-GNSS surveying methods, including total stations and laser scanners, involve time consuming practices in the field and costly equipment. The present study proposes the use of an Unmanned Aerial Vehicle (UAV) for autonomous rapid mapping that resolves the problem of localization for the drone itself by acquiring location information of characteristic points on the ground in a local coordinate system using simultaneous localization and mapping (SLAM) and vision algorithms. A common UAV equipped with a camera and at least a single known point, are enough to produce a local map of the scene and to estimate the relative coordinates of pre-defined ground points along with an additional arbitrary point cloud. The resulting point cloud is readily measurable for extracting and interpreting geometric information from the area of interest. Under two novel optimization procedures performing line and plane alignment of the UAV-camera-measured point geometries, a set of experiments determines that the localization of a visual point in distances reaching 15 m from the origin, delivered a level of accuracy under 50 cm. Thus, a simple UAV with an optical sensor and a visual marker, prove quite promising and cost-effective for rapid mapping and point localization in an unknown environment.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

2020 ◽  
Vol 50 (10) ◽  
pp. 1012-1024
Author(s):  
Meimei Wang ◽  
Jiayuan Lin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Individual Tree ◽  
Aerial Vehicle ◽  
The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

scholarly journals Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1651 ◽  
Author(s):  
Suk-Ju Hong ◽  
Yunhyeok Han ◽  
Sang-Yeon Kim ◽  
Ah-Yeong Lee ◽  
Ghiseok Kim
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Migratory Bird ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Detection Methods ◽  
Single Shot ◽  
Convolutional Network ◽  
Aerial Vehicle ◽  
Bird Detection

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.

scholarly journals Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 774 ◽  
Author(s):  
Sun ◽  
Wang ◽  
Ding ◽  
Lu ◽  
Sun
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Imaging System ◽  
Cloud Model ◽  
Tree Canopy ◽  
Coefficient Of Determination ◽  
Remote Measurement ◽  
Probability Density Estimation ◽  
Point Cloud Model ◽  
Aerial Vehicle

Information on fruit tree canopies is important for decision making in orchard management, including irrigation, fertilization, spraying, and pruning. An unmanned aerial vehicle (UAV) imaging system was used to establish an orchard three-dimensional (3D) point-cloud model. A row-column detection method was developed based on the probability density estimation and rapid segmentation of the point-cloud data for each apple tree, through which the tree canopy height, H, width, W, and volume, V, were determined for remote orchard canopy evaluation. When the ground sampling distance (GSD) was in the range of 2.13 to 6.69 cm/px, the orchard point-cloud model had a measurement accuracy of 100.00% for the rows and 90.86% to 98.20% for the columns. The coefficient of determination, R2, was in the range of 0.8497 to 0.9376, 0.8103 to 0.9492, and 0.8032 to 0.9148, respectively, and the average relative error was in the range of 1.72% to 3.42%, 2.18% to 4.92%, and 7.90% to 13.69%, respectively, among the H, W, and V values measured manually and by UAV photogrammetry. The results showed that UAV visual imaging is suitable for 3D morphological remote canopy evaluations, facilitates orchard canopy informatization, and contributes substantially to efficient management and control of modern standard orchards.

Numerical modeling of seismically-induced slope displacements: a comparison between 2D and 3D finite difference models and Newmark-Displacements

2020 ◽  
Author(s):  
Gisela Domej ◽  
Céline Bourdeau
Keyword(s):  
Finite Difference ◽  
Cross Section ◽  
3D Model ◽  
3D Models ◽  
Southern Spain ◽  
Longitudinal Cross Section ◽  
Landslide Body ◽  
Mesh Grid ◽  
2D And 3D ◽  
2D Model

<p>The majority of numerical landslide models are designed in 2D. In particular, models based on finite difference methods (FDM) are time-consuming and – as a result – in most cases also cost-intensive. 3D models, therefore, increase the processing time significantly. Another contributing factor to long processing times in the context of modeling of seismically-induced displacements is the fact that mesh grid increments must be small due to the necessity of correct wave propagation through the material. The larger the frequency range of the applied seismic signal should be, the smaller has to be the mesh grid increment. 3D models are, however, considered as more realistic.</p><p>In this work, we present a comprehensive study on numerical 2D and 3D models of the Diezma Landslide, Southern Spain. The Landslide is represented in its shape as it appeared at the time of the main rupture on 18<sup>th</sup> of March in four model layouts: (1) a simplified model in 3D that outlines the landslide body with planar triangular tiles, (2) a longitudinal cross section through this simplified 3D model representing the simplified 2D model, (3) a smooth model in 3D that envelops the landslide body according to the main topographic features, and (4) a longitudinal cross section through this smooth 3D model representing the smooth 2D model.</p><p>On both the simplified and the smooth 2D models, a series of 11 seismic scenarios was applied as SV-waves assuming a source sufficiently far for vertical incidence at the model bottoms in order to produce horizontal shear inside the landslide body with respect to the underlying bedrock. All 11 signals are characterized by different frequency contents, Arias Intensities from 0.1 to 1 m/s, moment magnitudes from 5.0 to 7.0 and peak ground accelerations from 0.8 to 1.2 m/s², and therefore correspond to scenarios that represent the local seismicity in Southern Spain.<br>Because of time-related limitations, only four of these signals were respectively applied to the simplified and smooth 3D model. Newmark-Displacements were calculated using all 11 signals with the classic Newmark-Method that approximates the landslide body in 2D by a rigid block on an inclined plane, and with Newmark’s Empirical Law as spatial information covering the landslide area across the slope in regular intervals.</p><p>We present a systematic comparison of all models and obtained displacements, showing that the Newmark-Methods deliver very similar results to the maximum displacements obtained by FDM. Moreover, we discuss on a particular example that – although seeming more accurate in the layout – smooth models lead not necessarily to realistic results.</p>

scholarly journals UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

2019 ◽  
Vol IV-2/W5 ◽  
pp. 405-412 ◽  
Author(s):  
A. Mayr ◽  
M. Bremer ◽  
M. Rutzinger ◽  
C. Geitner
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Test Site ◽  
Satellite System ◽  
Alpine Grassland ◽  
Proof Of Concept ◽  
Aerial Vehicle ◽  
Geomorphological Changes ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (<i>bare earth</i>, <i>grassland</i>, <i>trees</i>), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672&amp;thinsp;m<sup>3</sup> is estimated for the test site (48&amp;thinsp;ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland.</p>

scholarly journals Analysis Of Aerial Photography With Drone Type Fixed Wing In Kotabaru, Lampung

2018 ◽  
Vol 2 (1) ◽  
pp. 102-107
Author(s):  
Indreswari Suroso ◽  
Erwhin Irmawan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Aerial Photography ◽  
Ground Level ◽  
Aerial Photographs ◽  
Aerial Photos ◽  
Above Ground Level ◽  
The World ◽  
Air Capture ◽  
Aerial Vehicle ◽  
Operational Time

In the world of photography is very closely related to the unmanned aerial vehicle called drones. Drones mounted camera so that the plane is pilot controlled from the mainland. Photography results were seen by the pilot after the drone aircraft landed. Drones are unmanned drones that are controlled remotely. Unmanned Aerial Vehicle (UAV), is a flying machine that operates with remote control by the pilot. Methode for this research are preparation assembly of drone, planning altitude flying, testing on ground, camera of calibration, air capture, result of aerial photos and analysis of result aerial photos. There are two types of drones, multicopter and fixed wing. Fixed wing  has an airplane like shape with a wing system. Fixed wing use bettery 4000 mAh . Fixed wing drone in this research used   mapping in  This drone has a load ability of 1 kg and operational time is used approximately 30 minutes for an areas 20 to 50 hectares with a height of 100 m  to 200 m and payload 1 kg  above ground level. The aerial photographs in Kotabaru produce excellent aerial photographs that can help mapping the local government in the Kotabaru region.

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