Integration of point clouds from UAV photogrammetry and laserscan survey for the assessment of the risk of collapse of the vault of an underground cavity

2020 ◽  
Author(s):  
Davide Martinucci ◽  
Simone Pillon ◽  
Annelore Bezzi ◽  
Giulia Casagrande ◽  
Giorgio Fontolan ◽  
...  
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Control Point ◽  
Laser Scanner ◽  
Point Clouds ◽  
Integrated Approach ◽  
Surface Model ◽  
North East ◽  
Critical Issues ◽  
Gps Rtk

<p>Photogrammetric surveys from UAV and LiDAR surveys are two techniques that allow for the production of very high resolution point clouds. The use of these techniques result in a detailed reconstruction of difficult-to-access environments such as underground cavities. A rigorous georeferencing of the acquired data allows for a comparison of the hypogean development of the cave to the overlying territory. This study presents a case of integration between these two techniques, applied to the risk assessment of the collapse of the vaults in a natural cavity in the Trieste Karst (north east Italy). This site is particularly delicate given that on the slope above the cave there is an abandoned stone quarry. In order to survey the quarry above the cave, a flight was performed with UAV, while the cave was surveyed with Laser Scan from the ground. The flight was made using a UAV DJI Phantom RTK, which carried a 20 Mpixel 1“ sensor camera. 8 ha of terrain was surveyed, capturing about 733 high resolution images and surveying 22 GCPs (Ground Control Point) with a GNSS RTK receiver. It was possible to reduce the number of GCPs, since the drone recorded the shooting positions very accurately with the on-board GPS RTK. Data were analyzed using Agisoft Metashape Professional to produce an orthophoto and a DSM (Digital Surface Model) with a ground resolution of 0.02 m and 0.04 m respectively. The point cloud has a density of 586 points/m<sup>2</sup>. The LiDaR survey was carried out using an ILRIS 3D ER laser scanner from Optec. The point cloud has a density of approximately 2500 points/m<sup>2</sup> and 5 stations were needed to cover the underground development of the cavity. The georeferencing of the data was carried out by roto-translation on geo-referenced benchmarks, surveyed with GPS RTK and total station. The point cloud was processed using Terrascan software (Terrasolid). The two point clouds were aligned, geo-referenced and combined using Polyworks software (Innovmetric), in order to check the thicknesses of the material present above the vault of the cave. The integration of epigean and hypogean data made it possible to identify some critical points related to a vault thickness of approximately 1.5 meters, located at the quarry square. This work made it possible to highlight critical issues difficult to detect without the integrated approach of these different survey methodologies.</p>

scholarly journals POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1317-1323 ◽  
Author(s):  
G. Tran ◽  
D. Nguyen ◽  
M. Milenkovic ◽  
N. Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Surface Model ◽  
Knowledge Based ◽  
Full Waveform ◽  
Airborne Laser ◽  
Point Density ◽  
Area Classification

Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

Monitoring of a landslide through the use of UAV survey

2020 ◽  
Author(s):  
Simone Pillon ◽  
Davide Martinucci ◽  
Annelore Bezzi ◽  
Giulia Casagrande ◽  
Giorgio Fontolan ◽  
...  
Keyword(s):  
High Resolution ◽  
Control Point ◽  
Ground Control ◽  
Surface Model ◽  
Ground Control Point ◽  
North East ◽  
Active Portion ◽  
High Resolution Images ◽  
Precise Assessment ◽  
Friuli Venezia Giulia

<p>The monitoring of landslides using UAVs is particularly convenient as these are dangerous areas that present access difficulties. This study aims to integrate monitoring carried out via traditional techniques (GNSS and total station surveys of benchmarks) with UAV photogrammetric survey, as the latter allows for a precise assessment of the volumes affected by movement. The Masarach landslide, located in Friuli Venezia Giulia (north east Italy), covers an area of approximately 200 ha. Two surveys were carried out two years apart in order to measure displacements of much greater magnitude than instrumental errors. In the first survey, restricted to the most active area, a six rotor UAV was used, with a maximum take-off mass of 4 kg, which carried a 20 Mpixel APS-C camera. 243 high resolution images were captured and 27 GCPs (Ground Control Point) were surveyed with a GNSS RTK reciever. In the second survey a DJI Phantom 4 Pro UAV was used, carrying a 20 Mpixel 1“ sensor camera. 978 high resolution images were captured and 40 GCPs (Ground Control Point) were surveyed with a GNSS RTK reciever. Data were analyzed using Agisoft Metashape Professional to produce an orthophoto and a DSM (Digital Surface Model) with a ground resolution of 0.02 m and 0.04 m respectively. The DSMs were compared in ArcGIS to calculate the moving masses and highlight the areas of greatest instability. It emerged that approximately 10,000 cubic meters of landslide material were transported to the Arzino stream below, with verified displacements on the control point ranging from meters to centimeters. This work made it possible to accurately define the most active portion of the landslide.</p>

scholarly journals Strategies for 3D Modelling of Buildings from Airborne Laser Scanner and Photogrammetric Data Based on Free-Form and Model-Driven Methods: The Case Study of the Old Town Centre of Bordeaux (France)

2021 ◽  
Vol 11 (22) ◽  
pp. 10993
Author(s):  
Domenica Costantino ◽  
Gabriele Vozza ◽  
Vincenzo Saverio Alfio ◽  
Massimiliano Pepe
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Lidar ◽  
Free Form ◽  
Surface Model ◽  
Parametric Modelling ◽  
Model Driven ◽  
Town Centre

This paper presents a data-driven free-form modelling method dedicated to the parametric modelling of buildings with complex shapes located in particularly valuable Old Town Centres, using Airborne LiDAR Scanning (ALS) data and aerial imagery. The method aims to reconstruct and preserve the input point cloud based on the relative density of the data. The method is based on geometric operations, iterative transformations between point clouds, meshes, and shape identification. The method was applied on a few buildings located in the Old Town Centre of Bordeaux (France). The 3D model produced shows a mean distance to the point cloud of 0.058 m and a standard deviation of 0.664 m. In addition, the incidence of building footprint segmentation techniques in automatic and interactive model-driven modelling was investigated and, in order to identify the best approach, six different segmentation methods were tested. The segmentation was performed based on the footprints derived from Digital Surface Model (DSM), point cloud, nadir images, and OpenStreetMap (OSM). The comparison between the models shows that the segmentation that produces the most accurate and precise model is the interactive segmentation based on nadir images. This research also shows that in modelling complex structures, the model-driven method can achieve high levels of accuracy by including an interactive editing phase in building 3D models.

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>

Terrestrial-Aerial-SfM and TLS data fusion for agricultural terrace surveys in complex topographic and land cover conditions

2020 ◽  
Author(s):  
Sara Cucchiaro ◽  
Daniel J. Fallu ◽  
He Zhang ◽  
Kevin Walsh ◽  
Kristof Van Oost ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Point Clouds ◽  
Integrated Approach ◽  
Post Processing ◽  
Large Spatial Scale ◽  
North East ◽  
Study Sites ◽  
Uav Images ◽  
Survey Planning

&lt;p&gt;In the last two decades, important developments in High-Resolution Topographic (HRT) techniques, methods, sensors, and platforms has greatly improved our ability and opportunities for the characterization of landscapes through sub-metric DTMs (Digital Terrain Models). The choice of the most appropriate platform for HRT surveys must consider the required resolution, the spatial extent, and the features present in the analysed area. In complex topography, inaccessible areas and vegetated environments, the use of a single HRT technique is constrained by several factors. Therefore, data fusion from different acquisition platforms can be a useful solution if we design appropriate workflows for survey planning, data acquisition, post-processing, and uncertainty assessment. We tested this approach in the production of detailed DTMs of ancient agricultural terracing on two sites, Soave (North-east of Italy) and Martelberg in Saint-Martens-Voeren (East Belgium); case study sites for the TerrACE archaeological research project (ERC-2017-ADG: 787790, 2018-2023; https://www.terrace.no/). Both sites presented complex topographic and landcover conditions: the presence of vegetation (common in ancient, often abandoned, terraces) that cover parts of the sub-vertical surfaces (e.g., vertical walls of terraces), steep slopes and large survey areas. Therefore, we carefully designed the data fusion of HRT techniques in order to overcome all these constraints and thereby represent detailed 3D-views of the study sites. An integrated approach employing ground-based and UAV Structure from Motion (SfM) photogrammetry was used to preserve fine-grained topographic detail (via ground-based photos) and capture flat terrace zones at large spatial scale (via UAV images); while terrestrial Laser Scanner (TLS) permitted the accurate survey of the highly vegetated areas and vertical terrace walls. In order to create the point-cloud fusion, a key aspect for consideration when planning the survey planning was the location and distribution of the Ground Control Points (GCPs) for SfM and TLS targets. These are essential for georeferencing and co-registering of the aggregate data during the final merge. In the inaccessible zones of the studied areas, where was impossible to locate the GCPs, we tested the direct georeferencing of the UAV images with differential GNSS, such as PPK (post-processing kinematic). The SfM-TLS technologies allowed us to accurately recognize the topographic features of the entire terrace areas. This point-clouds merge was impossible to obtain without post-processing steps as co-registration process and uncertainty analysis. Even if several studies highlight how co-registration is essential in order to correctly merge HRT data, it is often not addressed in post-processing workflows. In this study, we demonstrated how survey planning and co-registration were fundamental phases for data fusion and allowed us to obtained proper and reliable DTMs. These high-resolution DTMs provided a high level of detail of landscape that was useful to extract valuable information about ancient terrace complexes: morphological features, profiles, sections and scaled plans, simplifying and speeding the archaeologist's field and laboratory work.&lt;/p&gt;

scholarly journals CORRECTION AND DENSIFICATION OF UAS-BASED PHOTOGRAMMETRIC THERMAL POINT CLOUD

2016 ◽  
Vol XLI-B3 ◽  
pp. 163-166
Author(s):  
O. Akcay ◽  
R. C. Erenoglu ◽  
O. Erenoglu
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Digital Camera ◽  
Point Clouds ◽  
Processing Parameters ◽  
Surface Model ◽  
Initial Image ◽  
Dense Point ◽  
Photogrammetric Method ◽  
Elevation Model

Photogrammetric processing algorithms can suffer problems due to either the initial image quality (noise, low radiometric quality, shadows and so on) or to certain surface materials (shiny or textureless objects). This can result in noisy point clouds and/or difficulties in feature extraction. Specifically, dense point clouds which are generated with photogrammetric method using a lightweight thermal camera, are more noisy and sparse than the point clouds of high-resolution digital camera images. In this paper, new method which produces more reliable and dense thermal point cloud using the sparse thermal point cloud and high resolution digital point cloud was considered. Both thermal and digital images were obtained with UAS (Unmanned Aerial System) based lightweight Optris PI 450 and Canon EOS 605D camera images. Thermal and digital point clouds, and orthophotos were produced using photogrammetric methods. Problematic thermal point cloud was transformed to a high density thermal point cloud using image processing methods such as rasterizing, registering, interpolation and filling. The results showed that the obtained thermal point cloud - up to chosen processing parameters - was 87% more densify than the original point cloud. The second improvement was gained at the height accuracy of the thermal point cloud. New densified point cloud has more consistent elevation model while the original thermal point cloud shows serious deviations from the expected surface model.

scholarly journals CORRECTION AND DENSIFICATION OF UAS-BASED PHOTOGRAMMETRIC THERMAL POINT CLOUD

2016 ◽  
Vol XLI-B3 ◽  
pp. 163-166
Author(s):  
O. Akcay ◽  
R. C. Erenoglu ◽  
O. Erenoglu
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Digital Camera ◽  
Point Clouds ◽  
Processing Parameters ◽  
Surface Model ◽  
Initial Image ◽  
Dense Point ◽  
Photogrammetric Method ◽  
Elevation Model

Photogrammetric processing algorithms can suffer problems due to either the initial image quality (noise, low radiometric quality, shadows and so on) or to certain surface materials (shiny or textureless objects). This can result in noisy point clouds and/or difficulties in feature extraction. Specifically, dense point clouds which are generated with photogrammetric method using a lightweight thermal camera, are more noisy and sparse than the point clouds of high-resolution digital camera images. In this paper, new method which produces more reliable and dense thermal point cloud using the sparse thermal point cloud and high resolution digital point cloud was considered. Both thermal and digital images were obtained with UAS (Unmanned Aerial System) based lightweight Optris PI 450 and Canon EOS 605D camera images. Thermal and digital point clouds, and orthophotos were produced using photogrammetric methods. Problematic thermal point cloud was transformed to a high density thermal point cloud using image processing methods such as rasterizing, registering, interpolation and filling. The results showed that the obtained thermal point cloud - up to chosen processing parameters - was 87% more densify than the original point cloud. The second improvement was gained at the height accuracy of the thermal point cloud. New densified point cloud has more consistent elevation model while the original thermal point cloud shows serious deviations from the expected surface model.

scholarly journals Fitting Terrestrial Laser Scanner Point Clouds with T-Splines: Local Refinement Strategy for Rigid Body Motion

2021 ◽  
Vol 13 (13) ◽  
pp. 2494
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Point Cloud ◽  
Goodness Of Fit ◽  
Laser Scanner ◽  
Point Clouds ◽  
Statistical Testing ◽  
Computational Time ◽  
Terrestrial Laser Scanner ◽  
Reference Surface ◽  
Local Refinement ◽  
Surface Approximation

T-splines have recently been introduced to represent objects of arbitrary shapes using a smaller number of control points than the conventional non-uniform rational B-splines (NURBS) or B-spline representatizons in computer-aided design, computer graphics and reverse engineering. They are flexible in representing complex surface shapes and economic in terms of parameters as they enable local refinement. This property is a great advantage when dense, scattered and noisy point clouds are approximated using least squares fitting, such as those from a terrestrial laser scanner (TLS). Unfortunately, when it comes to assessing the goodness of fit of the surface approximation with a real dataset, only a noisy point cloud can be approximated: (i) a low root mean squared error (RMSE) can be linked with an overfitting, i.e., a fitting of the noise, and should be correspondingly avoided, and (ii) a high RMSE is synonymous with a lack of details. To address the challenge of judging the approximation, the reference surface should be entirely known: this can be solved by printing a mathematically defined T-splines reference surface in three dimensions (3D) and modeling the artefacts induced by the 3D printing. Once scanned under different configurations, it is possible to assess the goodness of fit of the approximation for a noisy and potentially gappy point cloud and compare it with the traditional but less flexible NURBS. The advantages of T-splines local refinement open the door for further applications within a geodetic context such as rigorous statistical testing of deformation. Two different scans from a slightly deformed object were approximated; we found that more than 40% of the computational time could be saved without affecting the goodness of fit of the surface approximation by using the same mesh for the two epochs.

scholarly journals Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 201
Author(s):  
Michael Bekele Maru ◽  
Donghwan Lee ◽  
Kassahun Demissie Tola ◽  
Seunghee Park
Keyword(s):  
Optical Sensors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
3D Point Clouds ◽  
3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

scholarly journals VALIDATION OF A UAV-DERIVED POINT CLOUD BY SEMANTIC CLASSIFICATION AND COMPARISON WITH TLS DATA

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 83-90
Author(s):  
M. Franzini ◽  
V. Casella ◽  
P. Marchese ◽  
M. Marini ◽  
G. Della Porta ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Aerial Survey ◽  
Gradual Transition ◽  
Support Vector ◽  
Uniform Density ◽  
Mutual Distance ◽  
Semantic Classification ◽  
Cost Efficient

Abstract. Recent years showed a gradual transition from terrestrial to aerial survey thanks to the development of UAV and sensors for it. Many sectors benefited by this change among which geological one; drones are flexible, cost-efficient and can support outcrops surveying in many difficult situations such as inaccessible steep and high rock faces. The experiences acquired in terrestrial survey, with total stations, GNSS or terrestrial laser scanner (TLS), are not yet completely transferred to UAV acquisition. Hence, quality comparisons are still needed. The present paper is framed in this perspective aiming to evaluate the quality of the point clouds generated by an UAV in a geological context; data analysis was conducted comparing the UAV product with the homologous acquired with a TLS system. Exploiting modern semantic classification, based on eigenfeatures and support vector machine (SVM), the two point clouds were compared in terms of density and mutual distance. The UAV survey proves its usefulness in this situation with a uniform density distribution in the whole area and producing a point cloud with a quality comparable with the more traditional TLS systems.

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