Lightweight Unmanned Aerial Vehicle and Structure-from-Motion Photogrammetry for Generating Digital Surface Model for Open-Pit Coal Mine Area and Its Accuracy Assessment

2017 ◽  
pp. 17-33 ◽  
Author(s):  
Dieu Tien Bui ◽  
Nguyen Quoc Long ◽  
Xuan-Nam Bui ◽  
Viet-Nghia Nguyen ◽  
Chung Van Pham ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Coal Mine ◽  
Structure From Motion ◽  
Accuracy Assessment ◽  
Open Pit ◽  
Surface Model ◽  
Digital Surface Model ◽  
Mine Area ◽  
Aerial Vehicle

scholarly journals Accuracy assessment of open - pit mine’s digital surface models generated using photos captured by Unmanned Aerial Vehicles in the post - processing kinematic mode

2021 ◽  
Vol 62 (4) ◽  
pp. 38-47
Author(s):  
Long Quoc Nguyen ◽  
Keyword(s):  
Accuracy Assessment ◽  
Open Pit ◽  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Post Processing ◽  
Ground Control Points ◽  
Satellite Positioning ◽  
Surface Models ◽  
Aerial Vehicle

To evaluate the accuracy of the digital surface model (DSM) of an open-pit mine produced using photos captured by the unmanned aerial vehicle equipped with the post-processing dynamic satellite positioning technology (UAV/PPK), a DSM model of the Deo Nai open-pit coal mine was built in two cases: (1) only using images taken from UAV/PPK and (2) using images taken from UAV/PPK and ground control points (GCPs). These DSMs are evaluated in two ways: using checkpoints (CPs) and comparing the entire generated DSM with the DSM established by the electronic total station. The obtained results show that if using CPs, in case 1, the errors in horizontal and vertical dimension were 6.8 and 34.3 cm, respectively. When using two or more GCPs (case 2), the horizontal and vertical errors are at the centimetre-level (4.5 cm and 4.7 cm); if using the DSM comparison, the same accuracy as case 2 was also obtained.

Accuracy assessment using different UAV image overlaps

2019 ◽  
Vol 7 (3) ◽  
pp. 175-193
Author(s):  
Haval A. Sadeq
Keyword(s):  
Image Matching ◽  
Accuracy Assessment ◽  
3D Modelling ◽  
Surface Model ◽  
Digital Surface Model ◽  
Topographic Map ◽  
Aerial Vehicle ◽  
Close Range ◽  
Uav Image ◽  
Oblique Images

Unmanned aerial vehicle images are considered an important tool in close-range photogrammetry for topographic map production and 3D modelling using structure-from-motion approaches. The effect of overlap percentage in vertical and integrated vertical and oblique images on accuracy is evaluated. Analysis showed that the accuracy of the photogrammetric products (e.g., digital surface model and orthoimagery) is increased with the increased overlap percentage in vertical images. The accuracy is better when oblique images are integrated into vertical images than when only vertical images are used even with the same number of images. Furthermore, the building façade is constructed, but the building suffers from noise. Increasing the number of integrated vertical and oblique images improves the accuracy of the products and provides considerable precision to 3D modelling. This study showed that the improved result is due to the increased redundancy in image matching and optimised parameters of interior orientation through self-calibration. The images are processed using Pix4D software.

Evaluation of various setups of hydraulic structures in wild creek by River Flow2D

2020 ◽  
Author(s):  
Chin-Hsiang Tu ◽  
Hung-Pin Huang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
River Flow ◽  
Rainfall Data ◽  
Hydraulic Structures ◽  
Surface Model ◽  
Digital Surface Model ◽  
Check Dam ◽  
Aerial Photos ◽  
Aerial Vehicle ◽  
Movable Bed

<p>In Taiwan, the hydraulic structures of groundsill, check dam and embankment are frequently used in wild creek in order to prevent longitudinal and lateral scour. The benefit of these structures could not be numerically evaluated before construction without movable bed computational software. In recent years, the downstream scour-and-fill of hydraulic structures in wild creek could be carried out by software of River Flow 2D. This study used this software to evaluate the various setups of hydraulic structures in Jianshi, Hsinchu. Before carrying out software, the unmanned aerial vehicle (UAV) was operated to capture aerial photos of watershed. Then, the digital surface model (DSM) and orthomosaic photos were produced by Pix4Dmapper. Because most of wild creeks have no vegetation on their own creek bed, the DTM could be replaced by DSM. Associated with the various setups of hydraulic structures, Global mapper, QGIS and designed rainfall data, the software of River Flow 2D could give the downstream scour-and-fill of various setups of hydraulic structures. And, the convenient setup could be selected after evaluating the various setups of hydraulic structures.</p>

scholarly journals Accuracy Assessment of Post-processing Kinematic Georeferencing Based on Real-time Kinematic Unmanned Aerial Vehicle and Structure-from-motion Photogrammetry: Topographic Measurements of a Riverbed in a Small Watershed with a Check Dam

2021 ◽  
Author(s):  
Masato Hayamizu ◽  
Yasutaka Nakata
Keyword(s):  
Structure From Motion ◽  
Working Time ◽  
Surface Model ◽  
Digital Surface Model ◽  
Check Dam ◽  
Post Processing ◽  
Small Watershed ◽  
Total Station ◽  
Vertical Error ◽  
Aerial Vehicle

<p><a>To obtain an accurate digital surface model of the small watershed topography of a forested area while reducing time and labor costs, we used a consumer-grade unmanned aerial vehicle (UAV) with a build-in real-time kinematic global navigation satellite system. The applicability of structure-from-motion (SfM) multi-view stereo processing with post-processing kinematic (PPK) correction of the positional coordinate data (the UAV-PPK-SfM method) was tested. Nine verification points were set up in a small (0.5 km<sup>2</sup>) watershed, based on a check dam in the headwaters of a forest area. The location information of the verification points extracted from the digital surface model acquired by UAV-PPK-SfM and the overall working time were compared with the corresponding location information and working time of a traditional field survey using a total station. The results showed 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 UAV-PK-SfM survey was 10 % of that of the total station survey (30 min). The UAV-PPK-SfM workflow proposed in this study shows that wide-area, non-destructive topographic surveying, including fluvial geomorphological mapping, is possible with a vertical error of ±0.2 m in small watersheds (<0.5 km<sup>2</sup>). This method will be useful for rapid topographic surveying in inaccessible areas during disasters, such as monitoring debris flow at check dam sites, and for efficient topographic mapping of steep valleys in forested areas where the positioning of ground control points is a laborious task.</a></p>

scholarly journals Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification

2020 ◽  
Vol 12 (7) ◽  
pp. 1081 ◽  
Author(s):  
Mohamed Barakat A. Gibril ◽  
Bahareh Kalantar ◽  
Rami Al-Ruzouq ◽  
Naonori Ueda ◽  
Vahideh Saeidi ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Unmanned Aerial Vehicle ◽  
Urban Areas ◽  
Integrated Approach ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Surface Model ◽  
Urban Landscapes ◽  
Digital Surface Model ◽  
Aerial Vehicle

Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In this study, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale (MS) supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR orthomosaic and digital surface model (DSM). The integrated approach commenced through a preliminary MS image segmentation parameter selection, followed by the application of three supervised ML models, namely, random forest (RF), support vector machine (SVM), and decision tree (DT). These models were implemented at the optimal MS levels to identify preliminary information, such as the optimal segmentation level(s) and relevant features, for extracting 12 land use/land cover (LULC) urban classes from the fused datasets. Using the information obtained from the first phase of the analysis, detailed MS classification was iteratively conducted to improve the classification accuracy and derive the final urban LULC maps. Two UAV-based datasets were used to develop and assess the effectiveness of the proposed framework. The hierarchical classification of the pilot study area showed that the RF was superior with an overall accuracy (OA) of 94.40% and a kappa coefficient (K) of 0.938, followed by SVM (OA = 92.50% and K = 0.917) and DT (OA = 91.60% and K = 0.908). The classification results of the second dataset revealed that SVM was superior with an OA of 94.45% and K of 0.938, followed by RF (OA = 92.46% and K = 0.916) and DT (OA = 90.46% and K = 0.893). The proposed framework exhibited an excellent potential for the detailed mapping of heterogeneous urban landscapes from the fusion of UHSR orthophoto and DSM images using various ML models.

scholarly journals Accuracy Assessment of Post-processing Kinematic Georeferencing Based on Real-time Kinematic Unmanned Aerial Vehicle and Structure-from-motion Photogrammetry: Topographic Measurements of a Riverbed in a Small Watershed with a Check Dam

2021 ◽  
Author(s):  
Masato Hayamizu ◽  
Yasutaka Nakata
Keyword(s):  
Structure From Motion ◽  
Working Time ◽  
Surface Model ◽  
Digital Surface Model ◽  
Check Dam ◽  
Post Processing ◽  
Small Watershed ◽  
Total Station ◽  
Vertical Error ◽  
Aerial Vehicle

<p><a>To obtain an accurate digital surface model of the small watershed topography of a forested area while reducing time and labor costs, we used a consumer-grade unmanned aerial vehicle (UAV) with a build-in real-time kinematic global navigation satellite system. The applicability of structure-from-motion (SfM) multi-view stereo processing with post-processing kinematic (PPK) correction of the positional coordinate data (the UAV-PPK-SfM method) was tested. Nine verification points were set up in a small (0.5 km<sup>2</sup>) watershed, based on a check dam in the headwaters of a forest area. The location information of the verification points extracted from the digital surface model acquired by UAV-PPK-SfM and the overall working time were compared with the corresponding location information and working time of a traditional field survey using a total station. The results showed 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 UAV-PK-SfM survey was 10 % of that of the total station survey (30 min). The UAV-PPK-SfM workflow proposed in this study shows that wide-area, non-destructive topographic surveying, including fluvial geomorphological mapping, is possible with a vertical error of ±0.2 m in small watersheds (<0.5 km<sup>2</sup>). This method will be useful for rapid topographic surveying in inaccessible areas during disasters, such as monitoring debris flow at check dam sites, and for efficient topographic mapping of steep valleys in forested areas where the positioning of ground control points is a laborious task.</a></p>

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