scholarly journals POSSIBILITIES FOR USING LIDAR AND PHOTOGRAMMETRIC DATA OBTAINED WITH AN UNMANNED AERIAL VEHICLE FOR LEVEE MONITORING

2016 ◽  
Vol XLI-B1 ◽  
pp. 773-780 ◽  
Author(s):  
K. Bakuła ◽  
W. Ostrowski ◽  
M. Szender ◽  
W. Plutecki ◽  
A. Salach ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Digital Camera ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Lidar Data ◽  
Unmanned Aerial System ◽  
Navigation Systems ◽  
Control Points ◽  
Terrain Model ◽  
Increase In Accuracy

This paper presents the possibilities for using an unmanned aerial system for evaluation of the condition of levees. The unmanned aerial system is equipped with two types of sensor. One is an ultra-light laser scanner, integrated with a GNSS receiver and an INS system; the other sensor is a digital camera that acquires data with stereoscopic coverage. Sensors have been mounted on the multirotor, unmanned platform the Hawk Moth, constructed by MSP company. LiDAR data and images of levees the length of several hundred metres were acquired during testing of the platform. Flights were performed in several variants. Control points measured with the use of the GNSS technique were considered as reference data. The obtained results are presented in this paper; the methodology of processing the acquired LiDAR data, which increase in accuracy when low accuracy of the navigation systems occurs as a result of systematic errors, is also discussed. The Iterative Closest Point (ICP) algorithm, as well as measurements of control points, were used to georeference the LiDAR data. Final accuracy in the order of centimetres was obtained for generation of the digital terrain model. The final products of the proposed UAV data processing are digital elevation models, an orthophotomap and colour point clouds. The authors conclude that such a platform offers wide possibilities for low-budget flights to deliver the data, which may compete with typical direct surveying measurements performed during monitoring of such objects. However, the biggest advantage is the density and continuity of data, which allows for detection of changes in objects being monitored.

scholarly journals POSSIBILITIES FOR USING LIDAR AND PHOTOGRAMMETRIC DATA OBTAINED WITH AN UNMANNED AERIAL VEHICLE FOR LEVEE MONITORING

2016 ◽  
Vol XLI-B1 ◽  
pp. 773-780 ◽  
Author(s):  
K. Bakuła ◽  
W. Ostrowski ◽  
M. Szender ◽  
W. Plutecki ◽  
A. Salach ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Digital Camera ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Lidar Data ◽  
Unmanned Aerial System ◽  
Navigation Systems ◽  
Control Points ◽  
Terrain Model ◽  
Increase In Accuracy

This paper presents the possibilities for using an unmanned aerial system for evaluation of the condition of levees. The unmanned aerial system is equipped with two types of sensor. One is an ultra-light laser scanner, integrated with a GNSS receiver and an INS system; the other sensor is a digital camera that acquires data with stereoscopic coverage. Sensors have been mounted on the multirotor, unmanned platform the Hawk Moth, constructed by MSP company. LiDAR data and images of levees the length of several hundred metres were acquired during testing of the platform. Flights were performed in several variants. Control points measured with the use of the GNSS technique were considered as reference data. The obtained results are presented in this paper; the methodology of processing the acquired LiDAR data, which increase in accuracy when low accuracy of the navigation systems occurs as a result of systematic errors, is also discussed. The Iterative Closest Point (ICP) algorithm, as well as measurements of control points, were used to georeference the LiDAR data. Final accuracy in the order of centimetres was obtained for generation of the digital terrain model. The final products of the proposed UAV data processing are digital elevation models, an orthophotomap and colour point clouds. The authors conclude that such a platform offers wide possibilities for low-budget flights to deliver the data, which may compete with typical direct surveying measurements performed during monitoring of such objects. However, the biggest advantage is the density and continuity of data, which allows for detection of changes in objects being monitored.

scholarly journals TERRAIN EXTRACTION BY INTEGRATING TERRESTRIAL LASER SCANNER DATA AND SPECTRAL INFORMATION

2015 ◽  
Vol XL-2/W4 ◽  
pp. 45-51 ◽  
Author(s):  
C. L. Lau ◽  
S. Halim ◽  
M. Zulkepli ◽  
A. M. Azwan ◽  
W. L. Tang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Digital Camera ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Spectral Information ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
Terrain Model ◽  
Colour Image

The extraction of true terrain points from unstructured laser point cloud data is an important process in order to produce an accurate digital terrain model (DTM). However, most of these spatial filtering methods just utilizing the geometrical data to discriminate the terrain points from nonterrain points. The point cloud filtering method also can be improved by using the spectral information available with some scanners. Therefore, the objective of this study is to investigate the effectiveness of using the three-channel (red, green and blue) of the colour image captured from built-in digital camera which is available in some Terrestrial Laser Scanner (TLS) for terrain extraction. In this study, the data acquisition was conducted at a mini replica landscape in Universiti Teknologi Malaysia (UTM), Skudai campus using Leica ScanStation C10. The spectral information of the coloured point clouds from selected sample classes are extracted for spectral analysis. The coloured point clouds which within the corresponding preset spectral threshold are identified as that specific feature point from the dataset. This process of terrain extraction is done through using developed Matlab coding. Result demonstrates that a higher spectral resolution passive image is required in order to improve the output. This is because low quality of the colour images captured by the sensor contributes to the low separability in spectral reflectance. In conclusion, this study shows that, spectral information is capable to be used as a parameter for terrain extraction.

scholarly journals DTM GENERATION WITH UAV BASED PHOTOGRAMMETRIC POINT CLOUD

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 77-79 ◽  
Author(s):  
N. Polat ◽  
M. Uysal
Keyword(s):  
Low Cost ◽  
Laser Scanner ◽  
Digital Camera ◽  
Digital Terrain Model ◽  
Field Work ◽  
Generation Process ◽  
Control Points ◽  
Terrain Model ◽  
Digital Terrain ◽  
Ground Control Points

Nowadays Unmanned Aerial Vehicles (UAVs) are widely used in many applications for different purposes. Their benefits however are not entirely detected due to the integration capabilities of other equipment such as; digital camera, GPS, or laser scanner. The main scope of this paper is evaluating performance of cameras integrated UAV for geomatic applications by the way of Digital Terrain Model (DTM) generation in a small area. In this purpose, 7 ground control points are surveyed with RTK and 420 photographs are captured. Over 30 million georeferenced points were used in DTM generation process. Accuracy of the DTM was evaluated with 5 check points. The root mean square error is calculated as 17.1 cm for an altitude of 100 m. Besides, a LiDAR derived DTM is used as reference in order to calculate correlation. The UAV based DTM has o 94.5 % correlation with reference DTM. Outcomes of the study show that it is possible to use the UAV Photogrammetry data as map producing, surveying, and some other engineering applications with the advantages of low-cost, time conservation, and minimum field work.

scholarly journals Road Ditch Line Mapping with Mobile LiDAR

2021 ◽  
Author(s):  
Ayman Habib ◽  
◽  
Darcy M. Bullock ◽  
Yi-Chun Lin ◽  
Raja Manish
Keyword(s):  
Flow Direction ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Lidar Data ◽  
Design Standards ◽  
Cross Sectional ◽  
Terrain Model ◽  
State Highway ◽  
3D Point Clouds ◽  
Longitudinal Profiles

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires mapping of the ditch profile to identify areas requiring excavation of long-term sediment accumulation. High-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) provide an opportunity for effective monitoring of roadside ditches and performing hydrological analyses. This study evaluated the applicability of mobile LiDAR for mapping roadside ditches for slope and drainage analyses. The performance of alternative MLMS units was performed. These MLMS included an unmanned ground vehicle, an unmanned aerial vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system. Point cloud from all the MLMS units were in agreement in the vertical direction within the ±3 cm range for solid surfaces, such as paved roads, and ±7 cm range for surfaces with vegetation. The portable backpack system that could be carried by a surveyor or mounted on a vehicle and was the most flexible MLMS. The report concludes that due to flexibility and cost effectiveness of the portable backpack system, it is the preferred platform for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulders, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data, and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulder, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively.

scholarly journals COMPARISON OF UAV IMAGERY-DERIVED POINT CLOUD TO TERRESTRIAL LASER SCANNER POINT CLOUD

2019 ◽  
Vol IV-2/W5 ◽  
pp. 149-155 ◽  
Author(s):  
S. Peterson ◽  
J. Lopez ◽  
R. Munjy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Focal Length ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
Surface Model ◽  
Control Points ◽  
Ground Control Points ◽  
Aerial Vehicle

<p><strong>Abstract.</strong> A small unmanned aerial vehicle (UAV) with survey-grade GNSS positioning is used to produce a point cloud for topographic mapping and 3D reconstruction. The objective of this study is to assess the accuracy of a UAV imagery-derived point cloud by comparing a point cloud generated by terrestrial laser scanning (TLS). Imagery was collected over a 320&amp;thinsp;m by 320&amp;thinsp;m area with undulating terrain, containing 80 ground control points. A SenseFly eBee Plus fixed-wing platform with PPK positioning with a 10.6&amp;thinsp;mm focal length and a 20&amp;thinsp;MP digital camera was used to fly the area. Pix4Dmapper, a computer vision based commercial software, was used to process a photogrammetric block, constrained by 5 GCPs while obtaining cm-level RMSE based on the remaining 75 checkpoints. Based on results of automatic aerial triangulation, a point cloud and digital surface model (DSM) (2.5&amp;thinsp;cm/pixel) are generated and their accuracy assessed. A bias less than 1 pixel was observed in elevations from the UAV DSM at the checkpoints. 31 registered TLS scans made up a point cloud of the same area with an observed horizontal root mean square error (RMSE) of 0.006m, and negligible vertical RMSE. Comparisons were made between fitted planes of extracted roof features of 2 buildings and centreline profile comparison of a road in both UAV and TLS point clouds. Comparisons showed an average +8&amp;thinsp;cm bias with UAV point cloud computing too high in two features. No bias was observed in the roof features of the southernmost building.</p>

scholarly journals Integrated Laser Scanner Techniques to Produce High-Resolution DTM of Vegetated Territory

2021 ◽  
Vol 13 (13) ◽  
pp. 2504
Author(s):  
Federica Marotta ◽  
Simone Teruggi ◽  
Cristiana Achille ◽  
Giorgio Paolo Maria Vassena ◽  
Francesco Fassi
Keyword(s):  
High Resolution ◽  
Laser Scanner ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Terrain Model ◽  
Digital Terrain ◽  
3D Data ◽  
Detection And Identification ◽  
Geological Features ◽  
The Creation

The paper presents the first part of a research project concerning the creation of 3D terrain models useful to understand landslide movements. Thus, it illustrates the creation process of a multi-source high-resolution Digital Terrain Model (DTM) in very dense vegetated areas obtained by integrating 3D data coming from three sources, starting from long and medium-range Terrestrial Laser Scanner up to a Backpack Indoor Mobile Mapping System. The point clouds are georeferenced by means of RKT GNSS points and automatically filtered using a Cloth Simulation Filter algorithm to separate points belonging to the ground. Those points are interpolated to produce the DTMs which are then mosaicked to obtain a unique multi-resolution DTM that plays a crucial role in the detection and identification of specific geological features otherwise visible. Standard deviation of residuals of the DTM varies from 0.105 m to 0.176 m for Z coordinate, from 0.065 m to 0.300 m for X and from 0.034 m to 0.175 m for Y. The area under investigation belongs to the Municipality of Piuro (SO) and includes both the town and surrounding valley. It was affected by a dramatic landslide in 1618 that destroyed the entire village. Numerous challenges have been faced, caused both by the characteristics of the area and the processed data. The complexity of the case study turns out to be an excellent test bench for the employed technologies, providing the opportunity to precisely identify the needed direction to obtain future promising results.

scholarly journals Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters

2019 ◽  
Vol 11 (9) ◽  
pp. 1037 ◽  
Author(s):  
Shangshu Cai ◽  
Wuming Zhang ◽  
Xinlian Liang ◽  
Peng Wan ◽  
Jianbo Qi ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Parameter Tuning ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Terrain Model ◽  
Adaptive Parameter ◽  
Digital Terrain ◽  
Airborne Lidar Data

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

scholarly journals Detection of Levee Damage Based on UAS Data—Optical Imagery and LiDAR Point Clouds

2020 ◽  
Vol 9 (4) ◽  
pp. 248
Author(s):  
Krzysztof Bakuła ◽  
Magdalena Pilarska ◽  
Adam Salach ◽  
Zdzisław Kurczyński
Keyword(s):  
Laser Scanning ◽  
Vegetation Index ◽  
Laser Scanner ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Optical Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Levee Failure ◽  
Multi Temporal

This paper presents a methodology for levee damage detection based on Unmanned Aerial System (UAS) data. In this experiment, the data were acquired from the UAS platform, which was equipped with a laser scanner and a digital RGB (Red, Green, Blue) camera. Airborne laser scanning (ALS) point clouds were used for the generation of the Digital Terrain Model (DTM), and images were used to produce the RGB orthophoto. The main aim of the paper was to present a methodology based on ALS and vegetation index from RGB orthophoto which helps in finding potential places of levee failure. Both types of multi-temporal data collected from the UAS platform are applied separately: elevation and optical data. Two DTM models from different time periods were compared: the first one was generated from the ALS point cloud and the second DTM was delivered from the UAS Laser Scanning (ULS) data. Archival and new orthophotos were converted to Green-Red Vegetation Index (GRVI) raster datasets. From the GRVI raster, change detection for unvegetation ground areas was analysed using a dynamically indicated threshold. The result of this approach is the localisation of places, for which the change in height correlates with the appearance of unvegetation ground. This simple, automatic method provides a tool for specialist monitoring of levees, the critical objects protecting against floods.

scholarly journals Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

2021 ◽  
Vol 13 (13) ◽  
pp. 2485
Author(s):  
Yi-Chun Lin ◽  
Raja Manish ◽  
Darcy Bullock ◽  
Ayman Habib
Keyword(s):  
Flow Direction ◽  
Sediment Accumulation ◽  
Digital Terrain Model ◽  
Vertical Direction ◽  
Point Clouds ◽  
Lidar Data ◽  
Cross Sectional ◽  
Premature Failure ◽  
3D Point Clouds ◽  
Mapping Systems

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires a reasonably detailed mapping of the ditch profile to identify areas in need of excavation to remove long-term sediment accumulation. This study utilizes high-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) for mapping roadside ditches and performing hydrological analyses. The performance of alternative MLMS units, including an unmanned aerial vehicle, an unmanned ground vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system, is evaluated. Point clouds from all the MLMS units are in agreement within the ±3 cm range for solid surfaces and ±7 cm range for vegetated areas along the vertical direction. The portable backpack system that could be carried by a surveyor or mounted on a vehicle is found to be the most cost-effective method for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground-filtering approach—cloth simulation—is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from the LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data turned out to be very close to the highway cross slope design standards of 2% on driving lanes, 4% on shoulders, and a 6-by-1 slope for ditch lines.

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