scholarly journals THE USE OF UAS FOR RAPID 3D MAPPING IN GEOMATICS EDUCATION

2016 ◽  
Vol XLI-B6 ◽  
pp. 95-100
Author(s):  
Tee-Ann Teo ◽  
Peter Tian-Yuan Shih ◽  
Sz-Cheng Yu ◽  
Fuan Tsai
Keyword(s):  
Data Processing ◽  
Disaster Response ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
3D Mapping ◽  
Point Selection ◽  
Terrain Model ◽  
3D Point Clouds ◽  
Dense Point ◽  
Ground Point

With the development of technology, UAS is an advance technology to support rapid mapping for disaster response. The aim of this study is to develop educational modules for UAS data processing in rapid 3D mapping. The designed modules for this study are focused on UAV data processing from available freeware or trial software for education purpose. The key modules include orientation modelling, 3D point clouds generation, image georeferencing and visualization. The orientation modelling modules adopts VisualSFM to determine the projection matrix for each image station. Besides, the approximate ground control points are measured from OpenStreetMap for absolute orientation. The second module uses SURE and the orientation files from previous module for 3D point clouds generation. Then, the ground point selection and digital terrain model generation can be archived by LAStools. The third module stitches individual rectified images into a mosaic image using Microsoft ICE (Image Composite Editor). The last module visualizes and measures the generated dense point clouds in CloudCompare. These comprehensive UAS processing modules allow the students to gain the skills to process and deliver UAS photogrammetric products in rapid 3D mapping. Moreover, they can also apply the photogrammetric products for analysis in practice.

scholarly journals THE USE OF UAS FOR RAPID 3D MAPPING IN GEOMATICS EDUCATION

2016 ◽  
Vol XLI-B6 ◽  
pp. 95-100 ◽  
Author(s):  
Tee-Ann Teo ◽  
Peter Tian-Yuan Shih ◽  
Sz-Cheng Yu ◽  
Fuan Tsai
Keyword(s):  
Data Processing ◽  
Disaster Response ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
3D Mapping ◽  
Point Selection ◽  
Terrain Model ◽  
3D Point Clouds ◽  
Dense Point ◽  
Ground Point

With the development of technology, UAS is an advance technology to support rapid mapping for disaster response. The aim of this study is to develop educational modules for UAS data processing in rapid 3D mapping. The designed modules for this study are focused on UAV data processing from available freeware or trial software for education purpose. The key modules include orientation modelling, 3D point clouds generation, image georeferencing and visualization. The orientation modelling modules adopts VisualSFM to determine the projection matrix for each image station. Besides, the approximate ground control points are measured from OpenStreetMap for absolute orientation. The second module uses SURE and the orientation files from previous module for 3D point clouds generation. Then, the ground point selection and digital terrain model generation can be archived by LAStools. The third module stitches individual rectified images into a mosaic image using Microsoft ICE (Image Composite Editor). The last module visualizes and measures the generated dense point clouds in CloudCompare. These comprehensive UAS processing modules allow the students to gain the skills to process and deliver UAS photogrammetric products in rapid 3D mapping. Moreover, they can also apply the photogrammetric products for analysis in practice.

scholarly journals Photogrammetric surveying forests and woodlands with UAVs: techniques for automatic removal of vegetation and digital terrain model production for hydrological applications

2019 ◽  
Vol 7 (1) ◽  
pp. 1-20
Author(s):  
Fotis Giagkas ◽  
Petros Patias ◽  
Charalampos Georgiadis
Keyword(s):  
Vegetation Type ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Aerial Images ◽  
Surface Model ◽  
Terrain Model ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Dense Point ◽  
Height Model

The purpose of this study is the photogrammetric survey of a forested area using unmanned aerial vehicles (UAV), and the estimation of the digital terrain model (DTM) of the area, based on the photogrammetrically produced digital surface model (DSM). Furthermore, through the classification of the height difference between a DSM and a DTM, a vegetation height model is estimated, and a vegetation type map is produced. Finally, the generated DTM was used in a hydrological analysis study to determine its suitability compared to the usage of the DSM. The selected study area was the forest of Seih-Sou (Thessaloniki). The DTM extraction methodology applies classification and filtering of point clouds, and aims to produce a surface model including only terrain points (DTM). The method yielded a DTM that functioned satisfactorily as a basis for the hydrological analysis. Also, by classifying the DSM–DTM difference, a vegetation height model was generated. For the photogrammetric survey, 495 aerial images were used, taken by a UAV from a height of ∼200 m. A total of 44 ground control points were measured with an accuracy of 5 cm. The accuracy of the aerial triangulation was approximately 13 cm. The produced dense point cloud, counted 146 593 725 points.

scholarly journals Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2063 ◽  
Author(s):  
Zsuzsanna Szabó ◽  
Csaba Albert Tóth ◽  
Imre Holb ◽  
Szilárd Szabó
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Threshold Value ◽  
Noise Filtering ◽  
Terrain Model ◽  
Digital Terrain ◽  
Natural Neighbor ◽  
Wide Range ◽  
Ground Point

Airborne light detection and ranging (LiDAR) scanning is a commonly used technology for representing the topographic terrain. As LiDAR point clouds include all surface features present in the terrain, one of the key elements for generating a digital terrain model (DTM) is the separation of the ground points. In this study, we intended to reveal the efficiency of different denoising approaches and an easy-to-use ground point classification technique in a floodplain with fluvial forms. We analyzed a point cloud from the perspective of the efficiency of noise reduction, parametrizing a ground point classifier (cloth simulation filter, CSF), interpolation methods and resolutions. Noise filtering resulted a wide range of point numbers in the models, and the number of points had moderate correlation with the mean accuracies (r = −0.65, p < 0.05), indicating that greater numbers of points had larger errors. The smallest differences belonged to the neighborhood-based noise filtering and the larger cloth size (5) and the smaller threshold value (0.2). The most accurate model was generated with the natural neighbor interpolation with the cloth size of 5 and the threshold of 0.2. These results can serve as a guide for researchers using point clouds when considering the steps of data preparation, classification, or interpolation in a flat terrain.

Study on 3D Point Clouds Accuracy of Elongated Object Reconstruction in Close Range – Comparison of Different Software

2017 ◽  
Author(s):  
Grzegorz Gabara ◽  
Piotr Sawicki
Keyword(s):  
Data Processing ◽  
Test Object ◽  
Point Clouds ◽  
Geometric Parameters ◽  
Digital Data ◽  
Railway Track ◽  
Object Reconstruction ◽  
3D Point Clouds ◽  
Dense Point ◽  
Close Range

The image based point clouds generated from multiple different oriented photos enable 3D object reconstruction in a variety spectrum of close range applications. The paper presents the results of testing the accuracy the image based point clouds generated in disadvantageous conditions of digital photogrammetric data processing. The subject of the study was a long shaped object, i.e. the horizontal and rectilinear section of the railway track. DSLR Nikon D5100 camera, 16MP, equipped with the zoom lens (f = 18 ÷ 55mm), was used to acquire the block of terrestrial convergent and very oblique photos at different scales, with the full longitudinal overlap. The point clouds generated from digital images, automatic determination of the interior orientation parameters, the spatial orientation of photos and 3D distribution of discrete points were obtained using the successively tested software: RealityCapture, Photoscan, VisualSFM+SURE and iWitness+SURE. The dense point clouds of the test object generated with the use of RealityCapture and PhotoScan applications were filtered using MeshLab application. The geometric parameters of test object were determined by means of CloudCompare software. The image based dense point clouds allow, in the case of disadvantageous conditions of photogrammetric digital data processing, to determine the geometric parameters of a close range elongated object with the high accuracy (mXYZ &lt; 1 mm).

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.

Integrating acoustics and photogrammetry-based 3D point clouds for the generation of a continuous bathymetric model in coral reef environment.

2020 ◽  
Author(s):  
Alessandra Savini ◽  
Fabio Marchese ◽  
Luca Fallati ◽  
Cesare Corselli ◽  
Paolo Galli
Keyword(s):  
Coral Reef ◽  
Reef Flat ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Multibeam Echosounder ◽  
Terrain Model ◽  
Digital Terrain ◽  
3D Point Clouds ◽  
Reef Environments ◽  
Elevation Data

&lt;p&gt;Digital terrain model (DTM) reconstruction in coral reef environments through traditional mapping methods, using either singlebeam or multibeam echosounder systems, often presents difficulties in obtaining a continuous 3-dimensional representation, due to the complex topography and the considerable extension of very shallow areas (i.e. reef flat areas). The present-day most advanced techniques used to collect high-resolution elevation data both for land surface and the seafloor, in coral reef environments, include the use of satellite-derived bathymetry, LIDAR technology, Unmanned Aerial Vehicles coupled with photogrammetry and traditional bathymetric surveys. Data processing represents in all the cases a fundamental step for ensuring the accuracy and reliability of obtained measurements, especially for allowing a precise integration of all data sources into a continuous DTM. In our work, we present a tested methodological protocol for the generation of a continuous fine-scale digital terrain model (DTM) in coral reef environments. A portion of an atoll reef (Magoodhoo reef located in the Maldivian archipelago, the southern part of Faafu atoll) has been remotely mapped from the reef flat area to the connected and deeper lagoon environment, collecting elevation data by different sources according to the surveyed depths. In particular, we acquired acoustic depth measurements using a multibeam echosounder and 3D point clouds applying the Structure from Motion (SfM) technique to RGB images, collected using an Unmanned Aerial Vehicle (UAV). All obtained data were calibrated and validated with RTK-GNSS measurements and successfully integrated in order to generate a harmonized DTM for the surveyed sector of the Magoodhoo reef.&lt;/p&gt;

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.

scholarly journals Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

2019 ◽  
Vol 93 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Maria Immacolata Marzulli ◽  
Pasi Raumonen ◽  
Roberto Greco ◽  
Manuela Persia ◽  
Patrizia Tartarino
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Volume ◽  
Forest Plot ◽  
3D Point Clouds ◽  
Dense Point ◽  
Minimum Number ◽  
Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

scholarly journals Automated Generation of Digital Terrain Model using Point Clouds of Digital Surface Model in Forest Area

2011 ◽  
Vol 3 (5) ◽  
pp. 845-858 ◽  
Author(s):  
Kande R.M.U. Bandara ◽  
Lal Samarakoon ◽  
Rajendra P. Shrestha ◽  
Yoshikazu Kamiya
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Forest Area ◽  
Surface Model ◽  
Digital Surface Model ◽  
Automated Generation ◽  
Terrain Model ◽  
Digital Terrain

scholarly journals NEW DTM EXTRACTION APPROACH FROM AIRBORNE IMAGES DERIVED DSM

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 75-82 ◽  
Author(s):  
Y. A. Mousa ◽  
P. Helmholz ◽  
D. Belton
Keyword(s):  
Interpolation Method ◽  
Digital Terrain Model ◽  
Window Size ◽  
Group Iii ◽  
Terrain Model ◽  
Digital Terrain ◽  
Surface Models ◽  
Extraction Algorithm ◽  
Ground Point

In this work, a new filtering approach is proposed for a fully automatic Digital Terrain Model (DTM) extraction from very high resolution airborne images derived Digital Surface Models (DSMs). Our approach represents an enhancement of the existing DTM extraction algorithm <i>Multi-directional and Slope Dependent (MSD)</i> by proposing parameters that are more reliable for the selection of ground pixels and the pixelwise classification. To achieve this, four main steps are implemented: Firstly, 8 well-distributed scanlines are used to search for minima as a ground point within a pre-defined filtering window size. These selected ground points are stored with their positions on a 2D surface to create a network of ground points. Then, an initial DTM is created using an interpolation method to fill the gaps in the 2D surface. Afterwards, a pixel to pixel comparison between the initial DTM and the original DSM is performed utilising pixelwise classification of ground and non-ground pixels by applying a vertical height threshold. Finally, the pixels classified as non-ground are removed and the remaining holes are filled. The approach is evaluated using the Vaihingen benchmark dataset provided by the ISPRS working group III/4. The evaluation includes the comparison of our approach, denoted as Network of Ground Points (NGPs) algorithm, with the DTM created based on MSD as well as a reference DTM generated from LiDAR data. The results show that our proposed approach over performs the MSD approach.

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