Project F-I-80-4(148)148
Pin 10878
Silvercreek to Wanship MP 148.27—MP 154.97
Troy Peterson Project Manager, UDOT Region 2
Project Report Evaluate Use of the Supplied Mapping Grade LiDAR Point Cloud for Design
Jefferson L. Searle, PLS Project Manager Meridian Engineering, Inc. Ramesh Sridharan Chief Technologist Virtual Geomatics, Inc.
Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Executive Summary
Northern View, Taken During Control Survey
This project was designed to test the accuracy and road surface including edge of pavement and cost effectiveness of using an existing mapping grade, roadway striping. Supplemental surveying was performed on the vegetated slopes. mobile LiDAR dataset for design. After proper adjustment, the asset management, LiDAR point cloud can achieve accuracies useful for typical design activities for UDOT projects. An accuracy of 0.07 feet in the horizontal and the vertical was calculated. This is approaching the accuracies achieved during a typical GPS survey. Adjustment of the point cloud was done using preset control targets visible in the point cloud. Features were extracted from the point cloud for signs, barriers and other assets as well as all features on the Meridian Engineering
There was a twenty-four percent (24%) savings in cost and a and twenty-two percent (22%) time savings calculated when using the LiDAR data with supplemental surveying over traditional methods alone. There are several improvements or changes that could potentially be made to workflow or data collection methodology that may improve efficiency and savings. Additional research would be helpful to identify methods to maximize savings.
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
UDOT Point Cloud History Asset Management Project .............................................................................................................. 1 Use of LiDAR for Asset Management ............................................................................................... 1 Adding Value / Use of Data for Design ............................................................................................. 1
Surveying Technologies GPS and Total Station Surveying ...................................................................................................... 1 Photogrammetric Surveying ............................................................................................................ 1 Laser Scanning Surveying – LiDAR .................................................................................................... 2 General Strengths and Limitations ................................................................................................... 2
Preliminary Efforts Test Projects to Determine Design Suitability .................................................................................. 2 Establishing Control for Scan Calibration ......................................................................................... 2 I-15 in Salt Lake County .................................................................................................................... 2
I-80 Silvercreek to Wanship Project Overview .............................................................................................................................. 3 Project Team .................................................................................................................................... 3 Project Phases and Schedule ............................................................................................................ 3
Phase One—Adjustment and Verification Test Areas ......................................................................................................................................... 4 Laser Adjustment—Improve Relative Accuracy of Cloud ................................................................ 4 Cloud Calibration—Improve Absolute Accuracy of Cloud ............................................................... 4 Validation of Calibrated Point Cloud ................................................................................................ 5 Control Points ............................................................................................................................ 5 LiDAR ID Points .......................................................................................................................... 5 Test Areas .................................................................................................................................. 6 Delta Z Comparison ............................................................................................................. 6 Cross Section Analysis ......................................................................................................... 7 Limitations ........................................................................................................................................ 8
Phase Two—Surveying LiDAR Extraction ............................................................................................................................. 10 Supplemental Surveying ................................................................................................................ 10
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase Three—Analysis Accuracies Achieved .......................................................................................................................10 Comparison with Surveyed Features .............................................................................................10 Vertical Accuracy Check of Extracted Features ..............................................................................11 Horizontal Accuracy Check of Extracted Features .........................................................................11 Cost Comparison ............................................................................................................................12 Defining the Survey Area .........................................................................................................12 Labor Cost Comparison ............................................................................................................14
Lessons Learned Timeliness of Data ..........................................................................................................................14 Cross Sectional Extraction ..............................................................................................................14 Bridge Surveys ................................................................................................................................15
Conclusions Adjustment .....................................................................................................................................16 Accuracy .........................................................................................................................................16 Cost Savings ....................................................................................................................................16 Multiple Returns .............................................................................................................................16 Mapping Grade / Survey Grade ......................................................................................................16 Future Data Collection Schedule ....................................................................................................16
Appendices Appendix 1—Data Tables and Calculations ....................................................................................18 Delta Z Analysis ........................................................................................................................19 Phase 1—Point Clouds ......................................................................................................19 Phase 2—Extracted Features ............................................................................................23 Accuracy ...................................................................................................................................25 Cost ..........................................................................................................................................26 Appendix 2—Project Contacts .......................................................................................................28 Appendix 3—Maps .........................................................................................................................29 Project Overview .....................................................................................................................29 Phase 1—Delta Z Validation ....................................................................................................29 Phase 2—Area of Supplemental Survey ....................................................................................29
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
UDOT Point Cloud History The winning proposal was Mandli Communications, a firm that proposed using mapping grade mobile LiDAR and other vehicle mounted sensors to collect the required information. These other sensors provide a wealth of information for asset management. However, the LiDAR data was recognized as a potential source of information to designers and others inside UDOT.
Asset Management Project In the fall of 2011, the Utah Department of Transportation (UDOT), specifically the Division of Asset Management and the Department of Technology Services (DTS) issued a request for proposal for a “Roadway Imaging / Inventory Program” (solicitation JP12005).
The stated purpose of the RFP was to find a qualified firm to obtain pavement conditions information, and provide an inventory of specific roadway assets, roadside features, and safety elements for use in making pavement and roadway asset management decisions.
Adding Value / Use of Data for Design
Sample Asset
Use of LiDAR for Asset Management A large amount of flexibility was given to the proposing firms both in methodology and scope, to provide maximum value to the department. The data collection was to be done mostly at highway speeds, with minimal impacts to normal roadway operations.
To those who are or have been involved in preconstruction engineering it became apparent that many of the features necessary for design appear in the LiDAR data, often called a point cloud. However, even if these features do appear in the point cloud, it does not automatically follow that the data can be used for design. It remained to be determined if the necessary features are adequately represented in the data, and if they are spatially accurate and precise. To answer this question, it is useful to understand the way data is currently collected, to understand the methods employed, the accuracies involved, the nature of the resultant data set, and the limitations imposed by the technology. The following is a quick summary of different technologies and their accuracies:
Surveying Technologies just the features required, and to take relevant notes. This experience and proper coding minimizes office time. Total Stations require frequent, time consuming movement of equipment. Both Typical Accuracies* technologies can put personnel in Horiz. Vert. hazardous environments. Less experienced surveyors, incomplete 0.03 0.03 scoping, time or cost pressures can 0.04 0.08 result in missed features or rework. Trimble S6 Robotic Total Station
Photogrammetric Surveying
GPS and Total Station Surveying Although GPS and total stations use different technologies and yield results of different accuracies, they share many of the same advantages and disadvantages in comparison with other survey technologies. They are also the most utilized technologies currently deployed in UDOT surveys. Both technologies require surveyors in the field. This allows experienced technicians to be specific in selecting Meridian Engineering
Total Station - top & GPS - bottom
Photogrammetric surveys typically Typical Accuracies* are done from an aerial platform. It Horiz. Vert. is a remote sensing technology, 0.10 0.15 meaning the survey can be done away from traffic hazards. It allows 0.25’ pixel, aerial survey for rapid acquisition of survey data over large areas. The seasons, weather, sun angle and altitude constrain collection. Features that are not visible can’t be collected.
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Surveying Technologies to the data set. Large areas can be surveyed quickly. Terrestrial scanners are similar to reflectorless total stations in concept, although rather than a single observation, a scanner can make thousands of observations in the Typical Accuracies* same time frame. Scan data must Horiz. Vert. be post-processed to extract the 0.04 0.04 desired features from a cloud of Terrestrial Scanner millions of points.
General Strengths and Limitations Leica C10 LiDAR Terrestrial Laser Scanner
Laser Scanning Surveying – LiDAR Laser scanning can be done from static or mobile platforms. Static scanners typically are placed on a tripod similar to other survey instruments. Mobile scanners can be attached to a vehicle. Some of these are designed to be aerial sensors, others are road based. Scanners, just as GPS receivers, can be recreation grade, mapping grade or surveying grade, depending on accuracies needed. Like photogrammetry, scanning is remote sensing. In fact, most scanning is coupled with image acquisition to add richness
With traditional methods only those particular points and parts of features required, are collected. With LiDAR it is much less targeted. It is like casting a net over the job site. Many desirable points will be collected, as well as many undesired points. Angle points and other features may not be captured directly but will need to be interpolated from the massive volume of nearby observations. Scanning file sizes are many times larger than traditional survey files. *90 to 95 percent of observations employing the methods above will fall within the typical accuracies noted, these values are based on field experience, not manufacturer specifications and are expressed in feet.
Preliminary Efforts Test Projects to Determine Design Suitability The asset scanning was performed with a mapping grade scanner rather than a survey grade scanner, as using the data for design was not then envisioned. Asset Management met with Virtual Geomatics, that had developed algorithms to adjust mapping grade point cloud data to make it useful for design. An incredible amount of value for UDOT would be added to the point cloud, if this were possible. Central Office and Region Preconstruction were consulted and potential test projects were identified to evaluate the design suitability of the point cloud.
Existing I-80 Control Target
I-15 in Salt Lake County
Establishing Control for Scan Calibration While scanning was taking place an opportunity to set simple to use control was available for a few targeted areas. These control points are similar to aerial mapping targets (painted crosses on the road surface) and allow for the collected data to be translated to project coordinates. The targets are visible within the scan data and allow for Meridian Engineering
calibration and adjustment of the point cloud. The targets were set prior to scanning for a few test projects within the state, I-15 in Salt Lake County and I-80 in Summit County.
An early, limited test project was identified in Region Two, on a recently constructed portion of I-15. The design question was if there was room for an additional HOV lane between existing striping and the barrier wall. Some problems were identified concerning workflow and additional questions arose. A more robust, full scale test was planned on I-80 to scrutinize the data and process.
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
I-80 Silvercreek to Wanship Project Overview To fully test the utility of the point cloud for design, a 6.7 mile reconstruction project on I-80 in Summit County was selected. The project begins at Atkinson Bridge over Silvercreek and ends at the Wanship interchange (Mile Post 148.27 to 154.97). The project schedule provided an opportunity to test the LiDAR data. Paramount to UDOT’s concerns, a complete and Bridge Overpass Structure #0C 338 over I-80 at Tollgate Canyon Rd. - Promontory Ranch Rd. accurate design surface must be delivered to UDOT CADD Standards whether LiDAR data or surveying, coordinate systems, GIS and UDOT standards, traditional methods were used. A verification plan and was to set up the project coordinate system, provide workflow were established to determine if the point cloud verification that the LiDAR data was properly calibrated could be used for engineering design, or if a traditional with that system, verify the accuracies achieved, provide survey would be required. The workflow planning supplemental survey where the cloud was deemed included measuring actual costs of using the LiDAR data. deficient, and ensure all deliverables were in accordance with UDOT standards and expectations.
Project Team
Project Phases and Schedule
Region Two assigned Troy Peterson as Project Manager and selected Meridian Engineering, Inc. and Virtual Geomatics as a team to provide the project with design level survey. Virtual Geomatics, an expert with LiDAR data, was to calibrate the point cloud to the project’s coordinate system and adjust the data to improve its accuracy. If the point cloud was verified as accurate, survey features would be extracted from the cloud. Meridian, an expert in Schedule
The project was divided into three phases. The first phase was for surveying test areas and verifying the point cloud. The second phase was for performing any supplemental surveying needed after assessing the LiDAR data. The last phase was for analysis and for reporting the results. The project schedule was arranged around these milestones. The project began in April 2013 and ended in August 2013.
Adjustment & Verification Phase Apr 20, '13
May 3, '13
Surveying Phase
May 18, '13
May 31, '13
Jun 15, '13
Analysis Phase Jun 30, '13
Jul 17, '13
Aug 1, '13
Finish
Start
8/8/13
Calibrate Point-Cloud Identify Survey 4/15 to 4/26 Needs Verify Point-Cloud Accuracy 5/20 to 5/24 4/29 to 5/17 Atkinson Canyon
UP Rail Trail Silver Creek 80
Document Workflow 7/18 to 7/25
Supplemental & Design Surveys 5/28 to 7/17
Tollgate Canyon Rd.
Bridge # 4C 325
Test Area 2
Test Area 4
West Bound
East Bound MP 149
MP 150
UPPER CANYON (6500’ Elev.)
Meridian Engineering
Evaluation 8/2 to 8/8 Wanship Interchange
Test Area 6
Test Area 1 MP 148
Comparative Analysis 7/26 to 8/1
East Bound Promontory Ranch Rd.
Test Area 3 MP 151
Test Area 5 MP 152
MIDDLE CANYON (6300’ Elev.)
UDOT Region 2
MP 153
MP 154
80
MP 155
LOWER CANYON (6000’ Elev.)
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase One—Adjustment and Verification UDOT Mapping Grade Point Cloud Area of Test Area Survey (Traditional Methods)
Mile Posts
Area of Test Area Survey (Terrestrial LiDAR)
UDOT Control Points
UDOT Mapping Grade Point Cloud Test Area 4 - For Entire Map See - Project Overview Map - Appendix 1
Test Areas To be able to confirm the validity of the LiDAR calibration methods, six test areas were defined and surveyed using traditional methods. The test areas were arranged to encompass the varied conditions across the project. The project was divided into three segments, the upper canyon, middle canyon, and lower canyon.
each other. It is necessary to adjust the data from each laser to align with the data from the other lasers. Each point within the point cloud contains the identity of the laser that generated it, as an attribute. Using this information, the registration of one laser’s data relative to another can be resolved. This process improves the relative or internal accuracy, consolidating the data set.
In the upper canyon segment, which includes test areas one and two, the canyon walls are not as steep as the middle segment. The east and west bound lanes are widely separated by Silvercreek and the rail trail. The middle canyon segment has steep canyon walls and includes two bridges. It contains test areas three and four and has separated lanes. Test areas five and six are in the lower Before and After Laser Adjustment canyon segment, the canyon widens in this area, the east and west bound lanes are conjoined, being separated by a Cloud Calibration barrier wall.
Improve Absolute Accuracy of Point Cloud
Laser Adjustment Improve Relative Accuracy of Point Cloud The first step in adjusting the point cloud is to improve the internal or relative accuracy of the data. The LiDAR sensor used has sixty-four separate lasers that are each individually sending out pulses for data collection. This results in sixty-four similar data sets, disjointed relative to Meridian Engineering
Absolute accuracy refers to the position of a point in a global sense (its latitude and longitude), or how well any given point registers to the project coordinate system. This will be familiar to anyone who has tried to use GIS data or aerial imagery that was in a different coordinate system than was expected, or that has an unknown coordinate system. It just doesn’t line up properly with project data or other data sets that are known to be positionally accurate.
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase One—Adjustment and Verification calibration. Several steps were taken to ensure the point cloud was adjusted properly. First the accuracy was checked against the control points themselves. Next, the point cloud was checked against specific, individual survey observations. Ultimately, the data was checked across the entire data set in a few different ways using the six traditional (GPS and total station) test area surveys.
Control Points Before and After Cloud Calibration
This check has the highest precision but has the potential to be geographically limited to the area around the control The point cloud was not in the project coordinate system, point. The inverse distance between the control points, but this was not the only difficulty. Because the data was both in the project coordinate system and in the point not collected to be used cloud can be compared, to Before Point Cloud Calibration for design, design grade verify quality of the equipment and Registration of Cloud Control Average (US Feet) transformation and adjustment. procedures were not Points Before Matching This calculation was first done ΔX ΔY ΔZ used. The data had to be using all the control points with 4.064 1.813 2.368 transformed from the UTM Coordinates - GPS very high accuracy. A second 5.098 0.097 0.454 collection coordinate Project Coordinates - GPS transformation using only half system as well as adjusted Project Coord. - Total Station 5.050 0.165 0.399 the control points was done to along several vectors. test the accuracy of the process The point cloud was adjusted in four vectors or by checking against the other half of the points. The dimensions, x, y, z, and time. Time, as in travel time, resultant accuracies were comparable to a GPS survey. measured as the LiDAR LiDAR ID Points sensor traveled along the After Point Cloud Calibration roadway during scan Specific features visible in the Registration of Cloud Control Average (US Feet) collection. This was point cloud were then used to Points After Matching ΔX ΔY ΔZ accomplished by finding verify the point cloud accuracy. the control point targets UTM Coordinates - GPS 0.050 0.054 0.054 Features such as signs, in the point cloud and attenuators, walls, barriers, Project Coordinates - GPS 0.012 0.008 0.010 determining the delineators, and snow fences coordinates of the center Project Coord. - Total Station 0.010 0.006 0.012 were plainly visible in the point of the target in the point cloud. The various angle points After Calibration Using Half of the Control Points cloud coordinate system. and corners of these objects Average (US Feet) These values were Registration of Cloud Control were identified and surveyed in reconciled with the survey Points After Matching ΔX ΔY ΔZ the field. observations of the Originally the points on these Project Coordinates - GPS 0.044 0.050 0.057 targets, marked in their features were to be used to center by a small survey nail, in the project coordinate supplement the primary control points and improve the system. quality of the transformation. The observations on these Validation of Calibrated Point Cloud features supported the accuracies indicated by the control point verification. It was determined by the project team After the LiDAR point cloud was calibrated, as described that there would be no value, that is accuracy gains, in above, it was necessary to determine the quality of the adding these points into the transformation. Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase One—Adjustment and Verification
Two Post Sign
Single Post Sign
Attenuator
Snow Fence
Calibrated LiDAR Point Cloud and LiDAR ID Points used for verification of survey
similar the point cloud is to the test area survey.
The verification of the calibration and the accuracy of the point cloud was checked precisely against the control, and this check extended away from the control points using the LiDAR ID points. However, the accuracy check remained specific and not general to the entire cloud. In order to check all the points in the cloud, not just those on specific features, the test area surveys were processed per UDOT standards and a digital terrain model (DTM) created from these observations, the point cloud was then compared to this DTM.
The number of individual points in the LiDAR point cloud was over 289 million observations. In comparison there were only 11 thousand survey shots in the test area surveys. To do the delta Z comparison a representative sample of the point cloud of 1.44 million points was used.
Because a DTM is an elevation model, this check is a comparison of the vertical or Z coordinate of each point in the point cloud to the elevation represented by the DTM at the same X and Y coordinate as the point being checked. The inverse, or delta, between these two elevations allows for a generalized check of how Meridian Engineering
LiDAR - Vertical Accuracy
Test Areas - Delta Z Comparison
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase One—Adjustment and Verification The majority of all the points in the cloud are within accuracies useful for design, as can be seen in the accompanying charts.
Test Areas - Cross Section Analysis It is typical for a LiDAR point cloud to penetrate vegetation, at least in part. Laser returns will register on the top of the plants, on the bare earth, and everything in between. In order to penetrate the vegetation, the point cloud must retain multiple returns from the laser scanner.
LiDAR - Vertical Accuracy
The maps created by this process show that the data on the road surfaces are the most accurate, and that the majority of the least accurate points are on the vegetated slopes. This led to another check, specifically to address the slopes in the point cloud, and if the point cloud penetrated the vegetation.
Based on the results from the delta Z comparison, a few cross section surveys were performed. These cross sections required a survey observation every foot. In addition to typical observations on the ground, observations were made on the top of the vegetation as Delta Z Analysis used to Validate LiDAR Point Cloud
Trimble S6 Grid Survey
well. Despite the seasonal difference between LiDAR collection and the survey observations. It was clear that the LiDAR data did not penetrate the top of the vegetation. This explains the high delta z variation on the
See Appendix 2 for individual test area charts and data
side slopes where there appears to have been vegetation during the time of scan. This required supplemental surveying on the slopes.
Survey Breakline
Test Area 6
UDOT Control Points
Robotic Total Station & GPS Survey Cross Sections Test Area 4
Meridian Engineering
UDOT Region 2
For Full Maps See - Delta Z Validation Maps - Appendix 1
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase One—Adjustment and Verification Cross Section at MP 148.5 (Test Area 1)
LiDAR Data Colored by Elevation
As can be seen on the accompanying Delta Z charts and maps the adjusted LiDAR point cloud matches well on the roadway and other hard surfaces. The area where the survey surface is lower than the LiDAR data, are concentrated in specific locations on slopes and do not represent a randomness that would indicate noisy data. The cross section illustrated above was surveyed in *LiDAR data will often penetrate vegetation when multiple returns are stored by the sensor, but this also increases the size of the point cloud files
one of these areas (MP - 148.5). Survey shots were taken at ground level as is typical, and at the top of the vegetation. Despite the cross section being collected a year after and seasonably out of sync with the LiDAR data, it is clear the point cloud did not penetrate the vegetation* and is a major factor for high delta z variation in these areas. Survey Observations Top of Vegetation
LiDAR Data
Survey Observations at ground
multiple setups from difference angles. With mobile scanners care is taken to balance scanner height and There are generalized limitations to using LiDAR data for clearance, to maximize visibility for the sensor. Shadows design, specifically mobile data. These limitations affect due to obstructions are very common. With some this specific data set to obstructions, like a column, varying degrees. There are the shadow area moves Barrier Wall Shadows Vegetation Shadows also limitations specific to around the column as the this mobile LiDAR data set. scanner moves past. This These limitations include reduces the amount of shadowing effects, range coverage, but data is still limitations, multiple returns, available. Long linear and the timeliness of the features like walls will data. create a shadow behind the Shadows are the most wall along its entire length. common limitation of LiDAR Vegetation can also create data. The mitigation of shadow areas for similar Shadows Due to Slope shadow effects requires reasons, however its porous LiDAR Limitations - Shadows Caused by Sensor Obstructions careful planning and nature allows some systematic adjustment of penetration. The angle of collection technique. Shadow effects arise because laser the scanner can also create shadows where there are based equipment are essentially line of sight instruments. steep slopes that provide only a glancing angle for With total stations and tripod based scanners this requires collection.
Limitations
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase One—Adjustment and Verification Several factors affect the range of a laser based instrument. In general, however the signal accuracy diminishes with distance. This effect is magnified where the scan angle is less direct. This was apparent within the project data set when the east bound lanes of travel are side by side with the west bound lanes, separated by only a barrier, there was increased delta Z variability in the lanes opposite the scanner. In this area features for the westbound lanes were selected only from the westbound point cloud, and for the eastbound lanes from the eastbound point cloud. Multiple returns from any single laser, or the absence of multiple returns definitely puts limits on the use of the Demonstration of data. This also adds a limited amount to Multiple Returns scan file size. This LiDAR data was not originally intended for design, so it is understandable that multiple returns were not retained. With the successful repurposing of the cloud for design, the point cloud is limited where it was unable to penetrate the vegetation.
Changes to Road Surface
Meridian Engineering
Especially in the area around the Wanship interchange, new construction activity since the scans were collected put a limitation on the scan data. Construction actually resumed after the test area survey was completed, making that data equally obsolete. Additionally, an occasional delineator has been impacted by wayward traffic and replaced since scanning. Accuracy Decrease as Range Increases: Westbound Sensor
Accuracy Decrease as Range Increases: Eastbound Sensor
The project team decided to move forward and utilize the LiDAR data on the road surface and for select point features, like signs. The team decided supplemental surveying was needed to mitigate the data limitations, especially on the slopes. The resultant accuracies after eliminating vegetation, etc. are presented in the next section of this report.
For Full Map See - Delta Z Validation Map, Test Area 6 - Appendix 1
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase Two—Surveying LiDAR Extraction Extraction is the process of reducing the millions of LiDAR observations into the thousands of points needed to complete the survey, and create an accurate DTM. Some of these processes are automatic, some are computer assisted, and others require the purposeful selection of individual LiDAR points or the calculation of points inferred from the point cloud. Features extracted include striping, edge of pavement, tops and bottoms of barrier walls, fences, and other similar line features. Point features include signs, delineator posts, attenuators, catch basins, mile post markers and other similar objects. There were 19,777 points extracted from the point cloud, comprising 52 percent of all survey observations comprising the final survey.
Supplemental Surveying The slopes, ramps and drainage features were surveyed using traditional methods (GPS and total station survey)
52%
of final survey observations
48%
of final survey observations One Third of the Area Surveyed Extracted from Point Cloud Data
and then combined with the extracted point cloud data. The survey on the slopes accounted for two thirds of the survey effort, by area. However the point density is lower than that on the road surface. The number of points comprising the supplemental survey was 18,401 points. This makes up 48 percent of the total number of final survey observations.
Area of Supplemental Survey - For Entire Map See - Supplemental Survey Map - Appendix 1
Phase Three—Analysis project. Several analyses were done to support/verify these figures as outlined below.
Accuracies Achieved The accuracy of the point cloud data was calculated from the control points as seven hundredths of a foot (0.07 Calculated Accuracy feet) both horizontally and vertically. This approaches GPS Horiz. Vert. accuracies (0.04 feet horizontally and 0.08 feet vertically, for 90 0.07 0.07 95% of survey measurements). The Calibrated Mobile accuracies achieved are within the Scanning Data 96.3% of Control Points those needed for a typical design within noted accuracy
Meridian Engineering
Comparison with Surveyed Features Before extracting features from the cloud, comparisons between the point cloud and the survey observations had been similar to comparing apples and oranges. With extracted points the data sets are more similar in nature. This allows a more direct comparison of the data and of the usage of the point cloud data for design. The edge of pavement was collected with both survey techniques to
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase Three—Analysis supplement the verification beyond the test area surveys and allow for a comparison of the methods along the entire length of the project.
Major Contour: Cyan (Conventional Survey) Major Contour: Blue (LiDAR)
Minor Contour: Pink (Conventional Survey)
Minor Contour: Purple (LiDAR)
A primary comparison of the two datasets was achieved by processing the points through Bentley InRoads, using the typical workflow for UDOT projects. The resulting linework and contours were compared to identify their similarities and dissimilarities. The data lined up very well and did so across the entire project length. Some minor variation in striping elevations was noted and is likely due to the difference in reflectivity or intensity values between striping and pavement that are inherent with reflectorless measurements (LiDAR or Total Station).
Vertical Accuracy Check of Extracted Features
Comparison of Extracted Features and Test Area Survey Surface
As with the entire point cloud, a delta Z comparison of the extracted data with the test area surfaces was done. The percentage of data points that were within one tenth of a foot vertically from the test area surface was 93 percent. As noted on page 7, the percentage generated when comparing the entire point cloud was 83 percent within one tenth of a foot. The apparent improvement with the final extracted geometry of one tenth, indicates that the supplemental survey areas were properly selected, and that the hard surface observations are selected from some of the highest quality data in the point cloud.
Horizontal Accuracy Extracted Features
Check
of
The delta Z calculation created an indirect comparison of the horizontal accuracy of the point cloud. Where the extracted features are only a few thousand observations rather than millions, a different measure is helpful in verifying the horizontal accuracy of the extracted features. A comparison between test survey observations on roadway striping and the edge of asphalt can be made with the extracted line work for stripes and pavement edges.
Vertical Accuracy of Extracted Features - Delta Z Comparison with Test Areas
Meridian Engineering
UDOT Region 2
The horizontal distance was measured for each particular test area observation to the nearest extracted line feature from the point cloud. This proximity analysis is not as precise Page 11
Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase Three—Analysis as a calculation of accuracy with the control point check due to the width of striping. However, it does give a generalized indication of the extraction accuracy along the full width and length of the project. The distribution of points with the least similarity to the test area surveys indicate there is not a systematic problem with extraction. These points may represent a combination of the random errors of both methods of survey.
Check Shots
Extracted Features, Edge of Road, Striping
survey a project using GPS and total station, compared to the cost per mile of using features extracted from the asset management point cloud with supplemental survey on vegetated slopes. The cost is expressed in dollars per mile and also days per mile as a labor cost.
Defining the Survey Area
Defining the project length is more complicated than might be initially apparent. The centerline distance for Cost Comparison Horizontal Survey Check Shots on Extracted Striping Features the project is 6.7 miles. However, part of the project has the east bound lanes Accuracy is of course one of the primary concerns with the repurposing of the point cloud. Another key question is separated from the west bound lanes by hundreds of feet. whether there is added value in the point cloud due to As a result they are essentially two distinct surveys, reduced cost to survey a project. The measure to answer Segment 1 and Segment 2, each with a unique length. this question will be cost per mile. The cost per mile to There is an additional survey area, Segment 3, where both
West Bound Survey Area: Segment 2 East Bound Survey Area: Segment 1
Average Extraction Area Width: 40 feet
Co-directional Survey Area: Segment 3
Average Survey Width: 175 feet
Average Survey Width: 175 feet
Average Extraction Area Width: 80 feet East Bound Segment 1 and West Bound Segment 2
Meridian Engineering
Co-directional Segment 3
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase Three—Analysis Traditional Surveying - Length of Survey
Phase 1 Costs - Traditional Surveying Within the Test Areas
Direction / Segment
Length (feet)
Length (miles)
Survey Method
Cost
Cost / Mile
East Bound - Segment 1
21,493.0
4.071
Traditional
$34,398.27
$9,204.48
West Bound - Segment 2
22,737.5
4.306
Co-directional - Segment 3
12,245.5
2.319
Total
56,476.0
10.696
Phase 1 Length - Within the Test Areas
19,732.0
3.737
Survey Method
Cost
Cost / Mile
Phase 2 Length - Outside of the Test Areas
36,744.0
6.959
Supplemental
$34,359.82
$4,937.40
LiDAR Extraction
$17,962.83
$2,051.47
Total
$52,322.66
$6,988.88
Phase 2 Costs - LiDAR Extraction with Supplemental Surveying Outside of the Test Areas
Extraction Areas - Length of Survey Direction / Segment
Length (feet)
Length (miles)
East Bound
36,974.0
7.003
West Bound
37,176.0
7.041
Total
74,150.0
14.044
Phase 1 Length - Within the Test Areas
27,918.0
5.288
Phase 2 Length - Outside of the Test Areas
46,232.0
8.756
The lengths of each individual test area along with other supportive information can be found within Appendix 2
Cost Comparison
Project Phase
Phase 1: Surveying Within the Test Areas
Surveying Method
Cost / Mile
Phase 2: Surveying Outside of the Test Areas
Traditional Techniques
LiDAR Extraction with Supplemental Survey
$9,204.48
$6,988.88
Savings / Mile Using LiDAR Data
$2,215.60
Savings as Percentage
24%
Cost Effectiveness of Using LiDAR Point Cloud
Meridian Engineering
UDOT Region 2
sets of lanes come together to form one roadway separated by a barrier wall. All three survey segments are approximately 175 feet in width. The combined length of all three segments represent the length of the traditional survey. The length for LiDAR extraction was segmented differently. The average width of the extraction survey was forty feet, for either direction. The conjoined section of roadway was double that width, or eighty feet on average. The length for LiDAR extraction was thus an eastbound length plus a west bound length. Using these lengths and the costs for the various surveying activities the cost per mile was determined for each phase of the project. The test area survey that was the effort of phase one was done using traditional methods (total station and GPS). The combined cost for this phase of surveying included the time of all field crews, survey management, and CADD support for processing the survey data. The effort for phase two included supplemental surveying and the extraction of features from the LiDAR cloud for the survey outside the test areas. The survey costs for the supplemental work included field, survey management, and CADD support. The costs for extraction included all calibration and adjustment work done concurrently with phase one, the cost of extraction of line and point Page 13
Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Phase Three—Analysis features from the point cloud, and for the management of these activities. The calculation results appear in the accompanying tables. The findings indicate a savings of 24 percent for using the LiDAR data over traditional surveying alone. It should also be noted that the most costly and hazardous portion of the survey, that of the road surface itself, was not necessary as part of the phase two extraction and supplemental survey. Personnel were not as exposed to roadway dangers, making LiDAR surveys a safer alternative.
Labor Cost Comparison The labor cost, or the number of days per mile was calculated using the same survey areas and lengths used with the monetary cost savings calculation. The number of hours worked were converted to days using a typical eight hour day. The findings present a savings of 22 percent over traditional surveying.
For further comparison, two aerial mapping firms estimated cost of using aerial photogrammetry to prepare 7 miles of 400 foot wide, 0.25 foot pixel orthophotos, with planimetry mapping representing a 1-foot contour interval, was 10 to 15 thousand dollars.
Labor Cost Comparison Project Phase
Phase 1: Surveying Within the Test Areas
Surveying Method
Phase 2: Surveying Outside of the Test Areas
Traditional Techniques
LiDAR Extraction with Supplemental Survey
Days of Survey
60.6
113.8
Days / Mile
16.2
12.6
Savings - Days / Mile Using LiDAR Data
4.0
Savings as Percentage
22%
Labor Efficiency of Using LiDAR Point Cloud
Lessons Learned Timeliness of Data In processing the scan data there were several locations where changes to the roadway had occurred after the scan date. These areas required supplemental survey to deliver current mapping to the design team.
there were two small striping sections that appeared to be missing. One was found to be obstructed in the LiDAR data, the other indicated a patched section of roadway that, after a quick field check, still hasn’t been striped.
Cross Sectional Extraction Linear features like roadways are typically surveyed in cross section to create a representative elevation model.
The most extreme example is the Changes to Road Surface, page 9 pavement work done around the Wanship interchange. The bridge was replaced and the first phase of paving was done before scanning. The last phases of construction, including additional paving, was done after the test area survey was completed, making the traditional test area survey less representative of current conditions. A less extreme example would be road maintenance changes. A few delineators were obviously replaced after scanning, most likely after being damaged by traffic. During a quality review of the extraction it was noticed Meridian Engineering
UDOT Region 2
Comparison of Test Area and Extracted DTM Vertices
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Lessons Learned This connects observations along the axis of the road and is especially important as the distance increases between cross sections. The features extracted from the LiDAR were not done in cross section. However, the high density of the extracted points mitigated this. On future projects, collection of features in cross sections may improve terrain models.
Bridge Surveys Although bridge work was not originally planned for during this project, terrestrial LiDAR scans were collected on both bridges for an additional check on the mobile cloud. During a project meeting the structures group expressed some interest in working on one of the bridges.
If additional bridge data is required surveyors would not need to be sent out into the field. The bridge data that is necessary could be extracted in the office, minimizing delay for additional survey requests. Additionally using scanning technology on bridges results in reduced survey
Sample of a Bridge Data Collection Survey
Simultaneous Extraction of Multiple Bridge Features
Cross Sections of Terrestrial Scan Data on West Bound Bridge #4C 325 - Used for Bridge Feature Extraction
time. Collecting bridge data to the standards expected by the UDOT structures group using traditional technology requires approximately one week. The vast majority of that work is field time. The time required is cut in half when using terrestrial, survey-grade scanning technology, roughly evenly split between office and field.
In addition, questions that may arise that are answerable with the data extracted from the cloud data, or the image library with the scan can be processed to determine answers without additional field visits. Cost and time savings for bridge surveys using scanning technology was not included in the cost comparison for this report.
Meridian Engineering
UDOT Region 2
Scanning Tollgate Bridge
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Conclusions Adjustment The mobile scanning data for this project was collected using a sensor package for mapping purposes. Using the data for design was not intended by the data provider. Only later, to extract added value out of the cloud, did the interest in using the data for design arise. For land surveyors and mapping professionals, it is critical to understand that the mapping grade data was internally consistent and the data underlying the collection was available to aid in processing. The adjustments done to the data are analogous to a network adjustment or a level loop correction. The algorithms involved are, of course, not identical but the principles are the same. Adjusting bad data cannot yield better results. If the data from the scanner was not good data in a relative sense, the adjustments would not have yielded the same results.
Accuracy Using the coordinates of project control points, 96.3 percent of the control points met an accuracy of 0.07 feet in the horizontal and 0.07 feet the vertical. This calculation was supported by the delta Z analysis of the point cloud and positions on specific features identified in the LiDAR cloud (LiDAR ID points). Calculations using only the extracted points included refined vertical analysis (Delta Z) and a proximity analysis. These analyses showed the accuracies on the control points carried over to the entire point cloud, not just to the area localized around the control.
Cost Savings Adjusting the data did yield savings. Using the LiDAR data saves nearly one quarter of the cost and completes the project in about three fourths of the time compared to traditional surveying alone. The accuracies achieved have inspired other project teams to look to this process for design. There are some remaining items (see below) that, if addressed, could potentially increase the savings and value of using mobile LiDAR data for design.
Multiple Returns If the mapping data had retained the multiple returns received by the scanner (see page 9 for explanation) the value for design could have been even greater. It would likely cut the areas requiring supplemental survey to those Meridian Engineering
areas in the shadow regions caused by slope, barriers or other obstructions. Testing of data with the multiple returns intact would add a tremendous amount of understanding to the true value of adjusting this type of data. Mitigation of vegetation may also be enhanced by limiting scan times to seasonally ideal conditions or by mowing or cutting back vegetation.
Mapping Grade / Survey Grade It would be useful to know what the savings would have been if the original asset management scanning had been done with a survey grade scanner, rather than the sensor used. Several factors complicate answering this question. Survey grade scanners are more expensive, so the cost to gather data with them is more expensive. The file sizes and amount of data can be much larger. However, the data would not need as much adjusting to make the process work. Extraction costs would likely be similar.
Future Data Collection Schedule One of the major questions is how often the LiDAR data should be refreshed. Several ideas are now on the table these include: updating the data on a two year cycle, only acquiring new data for projects that are upcoming, or updating those areas that are most likely to change, like urban settings. Additionally, instituting a workflow to update the dataset after construction (with as-builts), or after maintenance activities may increase the shelf life of the data. This data is primarily used for asset management. If system wide changes were instituted to keep the asset data current and accurate the only consideration remaining would be the needs for design. Design needs accurate data. With the data currently available, supplemental surveying is necessary along the entire length of the corridor. Updating the areas that have changed may not be that big of a burden when supplemental surveying is still needed and crews are on site. However, there is tremendous potential for savings if the multiple returns are left intact. The impact of using data with multiple returns could be so substantial that it would be prudent to drive a sample of environments and conditions to determine how much change takes place over time, and how much impact having multiple returns would have on the savings to projects.
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Conclusions The data collection schedule for aerial image data sets may provide a pattern for developing an update schedule for the LiDAR data as it too covers a large area of the state. Aerial imagery is utilized on most projects, this data is occasionally flown for a specific project, however more often existing data is used more regularly. These images are flown over large areas of the state and shared across government and with the public for GIS and other purposes. The collection schedule and the resolution of the data is different for urban and rural areas. The Utah AGRC (Automated Geographic Resource Center), the state’s GIS data warehouse has imagery collected in the project area for 2011, 2009, 2006 (two data sets), 2004, and 2003. In contrast with downtown Salt Lake City there are additional data sets in 2009 and 2012. The accuracies or resolutions are higher in the data for Salt Lake City (and all along the Wasatch front). The high accuracy (sub-meter) data is
Meridian Engineering
available in 2012, 2009 and 2006. The high accuracy image sets are renewed every three years, but do not provide statewide coverage. For instance, near Canyonlands National Park there is not a single image data set with submeter coverage. To follow a similar data collection pattern for the mobile LiDAR data, the data would be collected state-wide less frequently. With more targeted or frequent collection in areas of significant or frequent change, every two or three years. The frequency of re-striping of the roads and the number of signs and other features that change from year to year will definitely be a major factor that influences the LiDAR collection schedule. Determining this frequency of change is beyond the scope of this project and will require additional research.
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Appendix 1—Data Tables & Calculations
View South From Tollgate Bridge, Taken During Proposal Preparations
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Delta Z Analysis - Phase 1 - Point Clouds
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Delta Z Analysis - Phase 1 - Point Clouds
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Delta Z Analysis - Phase 1 - Point Clouds
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Delta Z Analysis - Phase 1 - Point Clouds Comparison of Points Used for Delta Z Analysis Test Area
Area 1
Area 2
Area 3
Area 4
Area 5
Area 6
Number of LiDAR Points
328,791
170,785
108,683
227,971
274,829
331,771
Number of Conventional Survey Points
1,597
2,284
2,099
1,705
1,730
1,770
Delta Z Values classified in increments of 0.05 feet up to 0.50 feet - Underlying Data for Histogram Charts Class Name
Area 1
Area 2
Area 3
Area 4
Area 5
Area 6
# of points
% of points
# of points
% of points
# of points
% of points
# of points
% of points
# of points
% of points
# of points
% of points
beyond -0.50
17,895
5%
5,206
3%
4,663
4%
10,567
5%
13,479
5%
13,662
4%
-0.45 to -0.50
2,459
1%
487
0%
656
1%
947
0%
1,269
0%
1,705
1%
-0.40 to -0.45
2,330
1%
526
0%
776
1%
1,000
0%
1,266
0%
1,859
1%
-0.35 to -0.40
2,591
1%
506
0%
862
1%
1,061
0%
1,354
0%
1,977
1%
-0.30 to -0.35
2,759
1%
615
0%
924
1%
1,359
1%
1,348
0%
2,251
1%
-0.25 to -0.30
2,959
1%
656
0%
1,023
1%
1,483
1%
1,462
1%
2,541
1%
-0.20 to -0.25
3,088
1%
866
1%
1,322
1%
1,479
1%
1,528
1%
2,804
1%
-0.15 to -0.20
3,377
1%
1,557
1%
1,524
1%
1,666
1%
2,343
1%
3,226
1%
-0.10 to -0.15
4,585
1%
2,551
1%
1,723
2%
2,306
1%
5,633
2%
4,502
1%
-0.05 to -0.10
8,785
3%
3,909
2%
2,638
2%
4,399
2%
11,451
4%
6,607
2%
0.00 to -0.05
135,784
41%
67,461
40%
48,228
44%
100,676
44%
91,107
33%
85,984
26%
0.00 to 0.05
125,431
38%
73,275
43%
42,482
39%
89,308
39%
87,374
32%
119,901
36%
0.05 to 0.10
13,562
4%
9,036
5%
1,714
2%
6,079
3%
25,346
9%
38,559
12%
0.10 to 0.15
2,729
1%
3,077
2%
127
0%
2,226
1%
18,166
7%
21,516
6%
0.15 to 0.20
353
0%
728
0%
2
0%
1,397
1%
7,593
3%
13,891
4%
0.20 to 0.25
95
0%
88
0%
9
0%
713
0%
2,781
1%
6,401
2%
0.25 to 0.30
9
0%
45
0%
5
0%
480
0%
864
0%
2,113
1%
0.30 to 0.35
0
0%
38
0%
0
0%
307
0%
149
0%
890
0%
0.35 to 0.40
0
0%
30
0%
1
0%
181
0%
51
0%
672
0%
0.40 to 0.45
0
0%
19
0%
0
0%
117
0%
57
0%
444
0%
0.45 to 0.50
0
0%
22
0%
0
0%
98
0%
64
0%
117
0%
beyond 0.50
0
0%
87
0%
4
0%
122
0%
144
0%
149
0%
Total
328,791
100%
170,785
100%
108,683
100%
227,971
100%
274,829
100%
331,771
100%
Âą 0.05 feet
261,215
79%
140,736
82%
90,710
83%
189,984
83%
178,481
65%
205,885
62%
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Delta Z Analysis - Phase 1 - Point Clouds
Delta Z Values classified in increments of 0.05 feet up to 0.10 feet - Underlying Data for Pie Charts Class Name
Area 1
Area 2
Area 3
Area 4
Area 5
Area 6
# of points
% of points
# of points
% of points
# of points
% of points
# of points
% of points
# of points
% of points
# of points
% of points
beyond -0.10
42,043
13%
12,970
8%
13,473
12%
21,868
10%
29,682
11%
34,527
10%
-0.05 to -0.10
8,785
3%
3,909
2%
2,638
2%
4,399
2%
11,451
4%
6,607
2%
0.00 to -0.05
135,784
41%
67,461
40%
48,228
45%
100,676
44%
91,107
33%
85,984
26%
0.00 to 0.05
125,431
38%
73,275
43%
42,482
39%
89,308
39%
87,374
32%
119,901
36%
0.05 to 0.10
13,562
4%
9,036
5%
1,714
2%
6,079
3%
25,346
9%
38,559
12%
beyond 0.10
3,186
1%
4,134
2%
148
0%
5,641
2%
29,869
11%
46,193
14%
Total
328,791
100%
170,785
100%
108,683
100%
227,971
100%
274,829
100%
331,771
100%
Âą 0.05 feet
261,215
79%
140,736
82%
90,710
83%
189,984
83%
178,481
65%
205,885
62%
Delta Z Analysis - Phase 2 - Extracted Features
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Delta Z Analysis - Phase 2 - Extracted Features
Meridian Engineering
UDOT Region 2
Page 24
Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Accuracy After Point Cloud Calibration - Project Coordinates GPS - Comparison of Individual Observations Percent of Control Points in Range
Cumulative Percent
Percent of Control Points in Range
Cumulative Percent
Horizontal
Horizontal
Vertical
Vertical
> 0.09
1.85%
100.00%
0.00%
100.00%
0.07 - 0.09
1.85%
98.15%
3.70%
100.00%
0.05 - 0.07
5.56%
96.30%
1.85%
96.30%
0.03 - 0.05
9.26%
90.74%
0.00%
94.44%
0.01 - 0.03
24.07%
81.48%
22.22%
94.44%
<0.01
57.41%
57.41%
72.22%
94.44%
Accuracy Range
100.00%
Calculated Accuracy Horiz.
Vert.
0.07
0.07
Calibrated Mobile Scanning Data 96.3% of Control Points within noted accuracy
100.00%
After Calibration Using Half of the Control Points Statistic
ΔX
ΔY
ΔZ
Minimum
0.000
0.000
0.000
Maximum
0.161
0.286
0.223
Average
0.044
0.050
0.057
Calibrated Mobile Scanning Data Using Half of the Control Points
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Horizontal accuracies for each control point are calculated by finding the horizontal distance represented by the delta x and delta y.
Accuracy Check Horiz.
Vert.
0.07
0.06
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Cost Analysis Traditional Surveying - Length of Survey
Phase 1 Costs - Traditional Surveying Within the Test Areas
Direction / Segment
Length (feet)
Length (miles)
Survey Method
Cost
Cost / Mile
East Bound - Segment 1
21,493.0
4.071
Traditional
$34,398.27
$9,204.48
West Bound - Segment 2
22,737.5
4.306
Co-directional - Segment 3
12,245.5
2.319
Total
56,476.0
10.696
Phase 2 Costs - Supplemental Surveying Outside of the Test Areas
Phase 1 Length - Traditional Surveying Within Test Areas Test Area
Direction / Segment
Length (feet)
Length (miles)
Area 1
East Bound - Segment 1
3,675.5
0.696
Area 2
West Bound - Segment 2
3,605.0
0.683
Area 3
East Bound - Segment 1
2,576.0
0.488
Area 4
West Bound - Segment 2
2,504.0
0.474
Area 5
Co-directional - Segment 3
3,743.0
0.709
Area 6
Co-directional - Segment 3
3,628.5
0.687
19,732.0
3.737
Total
Survey Method
Cost
Cost / Mile
Supplemental
$34,359.82
$4,937.40
Per mile costs are derived by calculating cost per linear foot, then generalized to a per mile cost (by multiplying by 5,280).
Phase 2 Length - Supplemental Surveying Outside of the Test Areas Survey Area
Direction / Segment
Length (feet)
Length (miles)
Between Test Areas 1,3
East Bound - Segment 1
7,697.0
1.458
West of Test Area 2
West Bound - Segment 2
5,853.5
1.109
Between Test Areas 3,4
East Bound - Segment 1
7,544.5
1.429
Between Test Areas 2,4
West Bound - Segment 2
10,775.0
2.041
Between Test Areas 4,5
Co-directional - Segment 3
1,359.0
0.257
Between Test Areas 5,6
Co-directional - Segment 3
3,515.0
0.666
36,744.0
6.959
Total
Traditional Survey Areas Survey Area
Length (feet)
Length (miles)
Phase 1 - Within the Test Areas
19,732.0
3.737
Phase 2 - Outside of the Test Areas
36,744.0
6.959
Total
56,476.0
10.696
Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Cost Analysis Extraction Areas - Length of Survey
LiDAR Extraction Costs by Task
Direction / Segment
Length (feet)
Length (miles)
East Bound
36,974.0
7.003
West Bound
37,176.0
7.041
Total
74,150.0
14.044
Survey Area
Cost
Calibration and Adjustment
$11,390.00
Feature Extraction
$17,420.00
Total
$28,810.00
LiDAR Extraction Costs Relative to Test Areas Phase 1 Length - Extraction Within Test Areas
Survey Area
Cost
Test Area
Direction / Segment
Length (feet)
Length (miles)
Phase 1 - Within the Test Areas
$10,847.17
Phase 2 - Outside of the Test Areas
$17,962.83
Area 1
East Bound
3,675.5
0.696
Total
$28,810.00
Area 2
West Bound
3,605.0
0.683
Area 3
East Bound
2,576.0
0.488
Area 4
West Bound
2,504.0
0.474
Area 4
East Bound
814.5
0.154
Survey Method
Cost
Cost / Mile
Area 5
West Bound
3,743.0
0.709
Supplemental
$34,359.82
$4,937.40
Area 5
East Bound
3,743.0
0.709
LiDAR Extraction
$17,962.83
$2,051.47
Area 6
West Bound
3,628.5
0.687
Total
$52,322.66
$6,988.88
Area 6
East Bound
3,628.5
0.687
27,918.0
5.288
Total
Phase 2 Costs - LiDAR Extraction with Supplemental Surveying Outside of the Test Areas
Extraction Areas - Point Cloud Data Survey Area
Length (feet)
Length (miles)
Phase 1 - Within the Test Areas
27,918.0
5.288
Phase 2 - Outside of the Test Areas
46,232.0
8.756
Total
74,150.0
14.044
Cost Comparison
Project Phase Surveying Method
Cost / Mile
Meridian Engineering
Phase 1: Surveying Within the Test Areas
Phase 2: Surveying Outside of the Test Areas
Traditional Techniques
LiDAR Extraction with Supplemental Survey
$9,204.48
$6,988.88
Savings / Mile Using LiDAR Data
$2,215.60
Savings as Percentage
24%
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Appendix 2â&#x20AC;&#x201D;Project Contacts Consultant Management Team & Authors Leading the I-80 project team, Jeff oversaw the establishment of geodetic control, supplemental terrestrial laser scanning, survey data adjustment and integration of the supplemental survey data into the project deliverables. Working with Ramesh Sridharan of Virtual Geomatics, he developed the project workflow to validate the LiDAR data using ESRI GIS spatial analysis and then itâ&#x20AC;&#x2122;s final incorporation into a MicroStation/InRoads design surface in accordance with Utah Department of Transportation CADD Standards. Jeff has routinely served as a consultant project manager for surveying, mapping and right of way design projects and is an active member of the Utah Council of Land Surveyors. Jefferson L. Searle, PLS, Project Manager Meridian Engineering, Inc. Email: jsearle@merid-eng.com Ph: (801) 569-1315
Ramesh provided the I-80 team with technical support in utilizing Virtual Geomatics LiDAR point-cloud data processing software. He is a recognized leader in the commercial LiDAR processing software application development community creating both commercial and proprietary software applications in the areas of digital image processing and geospatial point-cloud software applications. Ramesh has provided consulting and implementation leadership for several major 3D mapping projects and an active contributing member of IEEE and the LAS specification (universal open point-cloud format) committees. Ramesh Sridharan, Chief Technologist Virtual Geomatics, Inc. Email: Ram@VirtualGeomatics.com Ph: (505) 463-3960
Principal Consultant Contacts Darryl Fenn, PLS, President Meridian Engineering, Inc. Email: dfenn@merid-eng.com Ph: (801) 569-1315
Kalvi Mani, CEO Virtual Geomatics, Inc. Email: kalvi.mani@virtualgeomatics.com Ph: (512) 524-2411
Utah Department of Transportation Region Two
Rebecka Stromness, PE, Preconstruction Support Environmental Manager Email: rstromness@utah.gov Mobile: (801) 887-3470
David Schwartz, PE, Program Manager Email: dschwartz@utah.gov Ph: (801) 887-3435 Troy Peterson, PE, Project Manager Email: tlpeterson@utah.gov Mobile: (801) 887-3637
Central Office Stan Burns, PE, Asset Management Director Email: sburns@utah.gov Ph: (801) 965-4150
Bradley Palmer, PE, Design Engineer Email: bgpalmer@utah.gov Ph: (801) 887-3632
Fred Doehring, PE. Deputy Engineer for Preconstruction Email: fdoehring@utah.gov Ph: (801) 633-6215
Vaughn Nelson, PE, Design Engineer Email: vanelson@utah.gov Ph: (801) 887-3404
Paul Wheeler, Technical Advancement Specialist Email: pwheeler@utah.gov Ph: (801) 965-4700
Jesse Sweeten, PE, Preconstruction Engineer Email: jsweeten@utah.gov Ph: (801) 975-4816 Meridian Engineering
UDOT Region 2
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Project Report | F-I-80-4(148)148 | Silvercreek to Wanship | August 8, 2013
Appendix 3—Maps
Southwestern View, Taken During Control Survey
Map Copies Original copies of this report contain a CD, on the inside back cover, with digital copies of the project maps in pdf format. If this report does not have the CD contact information for the authors appears in appendix 2. Current links to download the maps will then be provided.
Maps on the CD:
Project Overview Map
Phase 1 - Delta Z Validation Maps (Six maps, one for each test area)
Phase 2 - Supplemental Survey Map
Meridian Engineering
UDOT Region 2
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