Highway Cross-Slope Measurement using Mobile LiDAR

2018 ◽  
Vol 2672 (39) ◽  
pp. 88-97 ◽  
Author(s):  
Alireza Shams ◽  
Wayne A. Sarasua ◽  
Afshin Famili ◽  
William J. Davis ◽  
Jennifer H. Ogle ◽  
...  
Keyword(s):  
South Carolina ◽  
Laser Scanning ◽  
Effective Means ◽  
Cost Effective ◽  
Measurement Unit ◽  
Ground Control ◽  
Lidar Data ◽  
Ground Control Points ◽  
South Carolina Department ◽  
The Difference

Ensuring adequate pavement cross-slope on highways can improve driver safety by reducing the potential for ponding to occur or vehicles to hydroplane. Mobile laser scanning (MLS) systems provide a rapid, continuous, and cost-effective means of collecting accurate 3D coordinate data along a corridor in the form of a point cloud. This study provides an evaluation of MLS systems in terms of the accuracy and precision of collected cross-slope data and documentation of procedures needed to calibrate, collect, and process this data. Mobile light detection and ranging (LiDAR) data were collected by five different vendors on three roadway sections. The results indicate the difference between ground control adjusted and unadjusted LiDAR derived cross-slopes, and field surveying measurements less than 0.19% at a 95% confidence level. The unadjusted LiDAR data incorporated corrections from an integrated inertial measurement unit and high-accuracy real-time kinematic GPS, however it was not post-processed adjusted with ground control points. This level of accuracy meets suggested cross-slope accuracies for mobile measurements (±0.2%) and demonstrates that mobile LiDAR is a reliable method for cross-slope verification. Performing cross-slope verification can ensure existing pavement meets minimum cross-slope requirements, and conversely is useful in identifying roadway sections that do not meet minimum standards, which is more desirable than through crash reconnaissance where hydroplaning was evident. Adoption of MLS would enable the South Carolina Department of Transportation (SCDOT) to address cross-slope issues through efficient and accurate data collection methods.

scholarly journals Optimizing photogrammetric DEMs for glacier volume change assessment using laser-scanning derived ground-control points

2009 ◽  
Vol 55 (189) ◽  
pp. 106-116 ◽  
Author(s):  
Nicholas E. Barrand ◽  
Tavi Murray ◽  
Timothy D. James ◽  
Stuart L. Barr ◽  
Jon P. Mills
Keyword(s):  
Volume Change ◽  
Laser Scanning ◽  
Airborne Lidar ◽  
Ground Control ◽  
Lidar Data ◽  
Control Points ◽  
Change Models ◽  
Ice Caps ◽  
Ground Control Points ◽  
Stereo Imagery

AbstractPhotogrammetric processing of archival stereo imagery offers the opportunity to reconstruct glacier volume changes for regions where no such data exist, and to better constrain the contribution to sea-level rise from small glaciers and ice caps. The ability to derive digital elevation model (DEM) measurements of glacier volume from photogrammetry relies on good-quality, well-distributed ground reference data, which may be difficult to acquire. This study shows that ground-control points (GCPs) can be identified and extracted from point-cloud airborne lidar data and used to control photogrammetric glacier models. The technique is applied to midtre Lovénbreen, a small valley glacier in northwest Svalbard. We show that the amount of ground control measured and the elevation accuracy of GCP coordinates (based on known and theoretical error considerations) has a significant effect on photogrammetric model statistics, DEM accuracy and the subsequent geodetic measurement of glacier volume change. Models controlled with fewer than 20 lidar control points or GCPs from sub-optimal areas within the swath footprint overestimated volume change by 14–53% over a 2 year period. DEMs derived from models utilizing 20–25 or more GCPs, however, gave volume change estimates within ∼4% of those from repeat lidar data (−0.51 m a−1 between 2003 and 2005). Our results have important implications for the measurement of glacier volume change from archival stereo-imagery sources.

Spatial Accuracy of UAV-Derived Orthoimagery and Topography: Comparing Photogrammetric Models Processed with Direct Geo-Referencing and Ground Control Points

GEOMATICA ◽  
2016 ◽  
Vol 70 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Chris Hugenholtz ◽  
Owen Brown ◽  
Jordan Walker ◽  
Thomas Barchyn ◽  
Paul Nesbit ◽  
...  
Keyword(s):  
Satellite System ◽  
Base Station ◽  
Measurement Unit ◽  
Ground Control ◽  
Lower Grade ◽  
Control Points ◽  
Gps Receiver ◽  
Spatial Accuracy ◽  
Ground Control Points ◽  
Mean Square Errors

Mapping with unmanned aerial vehicles (UAVs) typically involves the deployment of ground control points (GCPs) to georeference the images and topographic model. An alternative approach is direct geo ref er encing, whereby the onboard Global Navigation Satellite System (GNSS) and inertial measurement unit are used without GCPs to locate and orient the data. This study compares the spatial accuracy of these approaches using two nearly identical UAVs. The onboard GNSS is the one difference between them, as one vehicle uses a survey-grade GNSS/RTK receiver (RTK UAV), while the other uses a lower-grade GPS receiver (non-RTK UAV). Field testing was performed at a gravel pit, with all ground measurements and aerial sur vey ing completed on the same day. Three sets of orthoimages and DSMs were produced for comparing spa tial accuracies: two sets were created by direct georeferencing images from the RTK UAV and non-RTK UAV and one set was created by using GCPs during the external orientation of the non-RTK UAV images. Spatial accuracy was determined from the horizontal (X,Y) and vertical (Z) residuals and root-mean-square-errors (RMSE) relative to 17 horizontal and 180 vertical check points measured with a GNSS/RTK base station and rover. For the two direct georeferencing datasets, the horizontal and vertical accuracy improved substantially with the survey-grade GNSS/RTK receiver onboard the RTK UAV, effectively reducing the RMSE values in X, Y and Z by 1 to 2 orders of magnitude compared to the lower grade GPS receiver onboard the non-RTK UAV. Importantly, the horizontal accuracy of the RTK UAV data processed via direct georeferencing was equivalent to the horizontal accuracy of the non-RTK UAV data processed with GCPs, but the vertical error of the DSM from the RTK UAV data was 2 to 3 times greater than the DSM from the non-RTK data with GCPs. Overall, results suggest that direct georeferencing with the RTK UAV can achieve horizontal accuracy comparable to that obtained with a network of GCPs, but for topographic meas urements requiring the highest achievable accuracy, researchers and practitioners should use GCPs.

scholarly journals Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device

Circulation ◽  
2021 ◽  
Author(s):  
John R. Giudicessi ◽  
Matthew Schram ◽  
J. Martijn Bos ◽  
Connor D. Galloway ◽  
Jacqueline B. Shreibati ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Rate ◽  
Qt Interval ◽  
Effective Means ◽  
Area Under The Curve ◽  
Cost Effective ◽  
Qtc Prolongation ◽  
Corrected Qt Interval ◽  
The Difference ◽  
Congenital Lqts

Background: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome (LQTS), and/or systemic diseases including SARS-CoV-2-mediated COVID19, can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead electrocardiogram (ECG) algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using over 1.6 million 12-lead ECGs from 538,200 patients, a deep neural network (DNN) was derived (n = 250,767 patients for training and n = 107,920 patients for testing) and validated (n = 179,513 patients) to predict the QTc using cardiologist over-read QTc values as the gold standard. The ability of this DNN to detect clinically-relevant QTc prolongation (e.g. QTc ≥ 500 ms) was then tested prospectively on 686 genetic heart disease (GHD) patients (50% with LQTS) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (-1.76 ± 23.14 ms). Similarly, within the prospective, GHD-enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (-0.45 ± 24.73 ms) and a commercial core ECG laboratory [+10.52 ms ± 25.64 ms] was nominal. When applied to mECG tracings, the DNN's ability to detect a QTc value ≥ 500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI-DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital LQTS in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

scholarly journals A LIGHT-WEIGHT LASER SCANNER FOR UAV APPLICATIONS

2016 ◽  
Vol XLI-B1 ◽  
pp. 711-715 ◽  
Author(s):  
A. M. G. Tommaselli ◽  
F. M. Torres
Keyword(s):  
Laser Scanning ◽  
Cost Benefit ◽  
Ground Control ◽  
Raspberry Pi ◽  
Weight System ◽  
Scanning System ◽  
Light Weight ◽  
Control Points ◽  
Ground Control Points ◽  
Flight Height

Unmanned Aerial Vehicles (UAV) have been recognized as a tool for geospatial data acquisition due to their flexibility and favourable cost benefit ratio. The practical use of laser scanning devices on-board UAVs is also developing with new experimental and commercial systems. This paper describes a light-weight laser scanning system composed of an IbeoLux scanner, an Inertial Navigation System Span-IGM-S1, from Novatel, a Raspberry PI portable computer, which records data from both systems and an octopter UAV. The performance of this light-weight system was assessed both for accuracy and with respect to point density, using Ground Control Points (GCP) as reference. Two flights were performed with the UAV octopter carrying the equipment. In the first trial, the flight height was 100 m with six strips over a parking area. The second trial was carried out over an urban park with some buildings and artificial targets serving as reference Ground Control Points. In this experiment a flight height of 70 m was chosen to improve target response. Accuracy was assessed based on control points the coordinates of which were measured in the field. Results showed that vertical accuracy with this prototype is around 30 cm, which is acceptable for forest applications but this accuracy can be improved using further refinements in direct georeferencing and in the system calibration.

scholarly journals GEO-REGISTERING CONSECUTIVE DATASETS BY MEANS OF A REFERENCE DATASET, ELIMINATING GROUND CONTROL POINT INDICATION

2019 ◽  
Vol XLII-5/W2 ◽  
pp. 85-91
Author(s):  
S. Vincke ◽  
M. Vergauwen
Keyword(s):  
Reference System ◽  
Laser Scanning ◽  
Control Point ◽  
Ground Control ◽  
Test Case ◽  
Second Phase ◽  
Construction Site ◽  
Reference Dataset ◽  
Ground Control Points ◽  
Additional Input

<p><strong>Abstract.</strong> The architecture, engineering and construction (AEC) industry’s interest in more advanced ways of regular monitoring of construction site activities and the achieved building progress has been rising recently. This requires frequent recordings of the area. This is only feasible if the profound observations only require limited time, both for the actual capturing on-site as well as processing of the recorded data. Moreover, for monitoring purposes, it is vital that all datasets use a single, unique reference system. This allows for an easy comparison of various observations to determine both building progress as well as possible construction deviations or errors.</p><p>In this work, a framework is proposed that facilitates a faster and more efficient way of co-registering or geo-registering consecutive datasets. It comprises three major stages, starting with the capturing of the surroundings of the construction site. By thoroughly adding numerous ground control points (GCPs) in a second phase, the processed result of this input data can be considered as a reference dataset. In a third stage, this known component is used as additional input for the processing of subsequently captured datasets. Using overlapping areas, the new observations can be immediately transferred to the correct reference system. This eliminates the indication of GCPs in subsequent datasets, which is known to be time-consuming and error-prone.</p><p>Although in this work the focus of the proposed framework lies on a photogrammetric recording approach, it also is applicable for laser scanning. Its potential is showcased on a real-world apartment construction site in Ghent, Belgium. In the test case, the presented approach is shown to be efficient, with comparable accuracies as other current methods, however, requiring less time and effort.</p>

scholarly journals COVID-19 Wastewater Epidemiology: A Model to Estimate Infected Populations

2020 ◽  
Author(s):  
Christopher Steven McMahan ◽  
Stella Self ◽  
Lior Rennert ◽  
Corey Kalbaugh ◽  
David Kriebel ◽  
...  
Keyword(s):  
South Carolina ◽  
Environmental Control ◽  
Cost Effective ◽  
Geographic Area ◽  
Mass Rate ◽  
Seir Model ◽  
Additional Tool ◽  
Department Of Health ◽  
South Carolina Department ◽  
Wastewater Samples

ABSTRACTBACKGROUNDWastewater-based epidemiology (WBE) provides an opportunity for near real-time, cost-effective monitoring of community level transmission of SARS-CoV-2, the virus that causes COVID-19. Detection of SARS-CoV-2 RNA in wastewater can identify the presence of COVID-19 in the community, but methods are lacking for estimating the numbers of infected individuals based on wastewater RNA concentrations.METHODSComposite wastewater samples were collected from three sewersheds and tested for SARS-CoV-2 RNA. A Susceptible-Exposed-Infectious-Removed (SEIR) model based on mass rate of SARS-CoV-2 RNA in the wastewater was developed to predict the number of infected individuals. Predictions were compared to confirmed cases identified by the South Carolina Department of Health and Environmental Control for the same time period and geographic area.RESULTSModel predictions for the relationship between mass rate of virus release to the sewersheds and numbers of infected individuals were validated based on estimated prevalence from individual testing. A simplified equation to estimate the number of infected individuals fell within the 95% confidence limits of the model. The unreported rate for COVID-19 estimated by the model was approximately 12 times that of confirmed cases. This aligned well with an independent estimate for the state of South Carolina.CONCLUSIONSThe SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed based on the mass rate of RNA copies released per day. This overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to better inform policy decisions.

scholarly journals Corridor Mapping of Sandy Coastal Foredunes with UAS Photogrammetry and Mobile Laser Scanning

2019 ◽  
Vol 11 (11) ◽  
pp. 1352 ◽  
Author(s):  
Alphonse Nahon ◽  
Pere Molina ◽  
Marta Blázquez ◽  
Jennifer Simeon ◽  
Sylvain Capo ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Management Practices ◽  
Control Point ◽  
Unmanned Aircraft ◽  
Ground Control ◽  
Scanning System ◽  
Control Points ◽  
Ground Control Points ◽  
Spatial Coverage ◽  
Mean Square Errors

Recurrent monitoring of sandy beaches and of the dunes behind them is needed to improve the scientific knowledge on their dynamics as well as to develop sustainable management practices of those valuable landforms. Unmanned Aircraft Systems (UAS) are sought as a means to fulfill this need, especially leveraged by photogrammetric and LiDAR-based mapping methods and technology. The present study compares different strategies to carry UAS photogrammetric corridor mapping over linear extensions of sandy shores. In particular, we present results on the coupling of a UAS with a mobile laser scanning system, operating simultaneously in Cap Ferret, SW France. This aerial-terrestrial tandem enables terrain reconstruction with kinematic ground control points, thus largely avoiding the deployment of surveyed ground control points on the non-stable sandy ground. Results show how these three techniques—mobile laser scanning, photogrammetry based on ground control points, and photogrammetry based on kinematic ground control points—deliver accurate (i.e., root mean square errors < 15 cm) 3D reconstruction of beach-to-dune transition areas, the latter being performed at lower survey and logistic costs, and with enhanced spatial coverage capabilities. This study opens the gate for exploring longer (hundreds of kilometers) shoreline dynamics with ground-control-point-free air and ground mapping techniques.

scholarly journals Nighttime Mobile Laser Scanning and 3D Luminance Measurement: Verifying the Outcome of Roadside Tree Pruning with Mobile Measurement of the Road Environment

2020 ◽  
Vol 9 (7) ◽  
pp. 455
Author(s):  
Mikko Maksimainen ◽  
Matti T. Vaaja ◽  
Matti Kurkela ◽  
Juho-Pekka Virtanen ◽  
Arttu Julin ◽  
...  
Keyword(s):  
Measurement System ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Measurement Unit ◽  
Mobile Mapping ◽  
Camera System ◽  
Roadside Vegetation ◽  
Mapping System ◽  
Mobile Mapping System ◽  
The Difference

Roadside vegetation can affect the performance of installed road lighting. We demonstrate a workflow in which a car-mounted measurement system is used to assess the light-obstructing effect of roadside vegetation. The mobile mapping system (MMS) includes a panoramic camera system, laser scanner, inertial measurement unit, and satellite positioning system. The workflow and the measurement system were applied to a road section of Munkkiniemenranta, Helsinki, Finland, in 2015 and 2019. The relative luminance distribution on a road surface and the obstructing vegetation were measured before and after roadside vegetation pruning applying a luminance-calibrated mobile mapping system. The difference between the two measurements is presented, and the opportunities provided by the mobile 3D luminance measurement system are discussed.

scholarly journals PARAMETERIC ANALYSIS FOR AUTOMATED EXTRACTION OF ROAD EDGES FROM MOBILE LASER SCANNING DATA

2015 ◽  
Vol II-2/W2 ◽  
pp. 215-221 ◽  
Author(s):  
P. Kumar ◽  
P. Lewis ◽  
C. P. McElhinney
Keyword(s):  
Laser Scanning ◽  
Cost Effective ◽  
Lidar Data ◽  
Automated Algorithm ◽  
Accurate Knowledge ◽  
3D Data ◽  
Left And Right ◽  
Optimal Values ◽  
Raster Cell ◽  
Accuracy Performance

The applicability of Mobile Laser Scanning (MLS) systems continue to prove their worth in route corridor mapping due to the rapid, continuous and cost effective 3D data acquisition capability. LiDAR data provides a number of attributes which can be useful for extracting various road features. Road edge is a fundamental feature and its accurate knowledge increases the reliability and precision of extracting other road features. We developed an automated algorithm for extracting left and right edges from MLS data. The algorithm involved several input parameters which are required to be analysed in order to find their optimal values. In this paper, we present a detailed analysis of the dimension parameters of input data and raster cell in our algorithm. These parameters were analysed based on temporal, completeness and accuracy performance of our algorithm for their different sets of values. This analysis provided the estimation of an optimal values of parameters which were used to automate the process of extracting road edges from MLS data.

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