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 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 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 COMPARISON OF 3D POINT CLOUDS FROM AERIAL STEREO IMAGES AND LIDAR FOR FOREST CHANGE DETECTION

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 1-5 ◽  
Author(s):  
D. Ali-Sisto ◽  
P. Packalen
Keyword(s):  
Classification Accuracy ◽  
Digital Terrain Model ◽  
Ground Level ◽  
Point Clouds ◽  
Aerial Image ◽  
Lidar Data ◽  
Forest Change ◽  
Time Points ◽  
3D Point Clouds ◽  
The Difference

This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.

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 SimpleForest - a comprehensive tool for 3d reconstruction of trees from forest plot point clouds

2021 ◽  
Author(s):  
Jan Hackenberg ◽  
Kim Calders ◽  
Miro Demol ◽  
Pasi Raumonen ◽  
Alexandre Piboule ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Forest Plot ◽  
Data Sets ◽  
Cross Sectional ◽  
Data Set ◽  
Terrain Model ◽  
Digital Terrain ◽  
Pipe Model ◽  
The One

The here-on presented SimpleForest is written in C++ and published under GPL v3. As input data SimpleForest utilizes forestry scenes recorded as terrestrial laser scan clouds. SimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders. These QSMs allow us to calculate traditional forestry metrics such as diameter at breast height, but also volume and other structural metrics that are hard to measure in the field. Our volume evaluation on three data sets with destructive volumes show high prediction qualities with concordance correlation coefficient CCC (r2 adj.) of 0.91 (0.87), 0.94 (0.92) and 0.97 (0.93) for each data set respectively. We combine two common assumptions in plant modeling The sum of cross sectional areas after a branch junction equals the one before the branch junction (Pipe Model Theory) and Twigs are self-similar (West, Brown and Enquist model). As even sized twigs correspond to even sized cross sectional areas for twigs we define the Reverse Pipe Radius Branchorder (RPRB) as the square root of the number of supported twigs. The prediction model radius = B 0 ∗ RP RB relies only on correct topological information and can be used to detect and correct overestimated cylinders. In QSM building the necessity to handle overestimated cylinders is well known. The RPRB correction performs better with a CCC (r2 adj.) of 0.97 (0.93) than former published ones 0.80 (0.88) and 0.86 (0.85) in our validation. We encourage forest ecologists to analyze output parameters such as the GrowthVolume published in earlier works, but also other parameters such as the GrowthLength, VesselVolume and RPRB which we define in this manuscript.

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 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.

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

<p>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.</p>

scholarly journals INTERACTIVE EXTRACTION OF LINEAR STRUCTURES FROM LIDAR RAWDATA FOR ARCHAEOMORPHOLOGICAL STRUCTURE PROSPECTION

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 153-161
Author(s):  
P. Even ◽  
A. Grzesznik ◽  
A. Gebhardt ◽  
T. Chenal ◽  
P. Even ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Extraction Process ◽  
Lidar Data ◽  
Visualization Tool ◽  
Linear Structures ◽  
Archaeological Prospection ◽  
Terrain Model ◽  
Digital Terrain ◽  
On Line ◽  
Longitudinal Profiles

Abstract. A new detection and visualization tool to inspect raw LiDAR data for archaeological prospection is introduced in this paper. It allows the supervised extraction of linear structures (ridge or hollow) from the 3D ground points, for on-line detailed analysis of their cross and longitudinal profiles. Using raw data provides a richer information than an interpolated digital terrain model. In particular, the extraction process is made aware of point repartition irregularities caused by dense canopies in forested environments. The tool is based on a recent curvilinear structure extraction framework with fast execution time, that ensures a good interaction. Additional performance is achieved through the detection of the local terrain trend around the structure, that allows finer characterizations of the extracted structure. The suitability to several application purposes has been evaluated by archaeologists through real context experiments. The tool was first applied to the survey of a well-known medieval wall and to the identification of its less preserved parts, that are still undisclosed in the forested landscape. Then it was used in the scope of a prospective work about man impacts on its environment to detect and analyze old holloways and to get a better understanding of their local sunkness or the cause of their local deviations. Potential and limits of the tools are discussed. Open source and executable codes are left available for more extensive exploitation and possible integration into GIS softwares.

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.

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