LIDAR DATA ACQUISITION AND MAPPING FOR CHICAGO’S CREATE PROGRAM

AREMA Conference 2005 W. Robert Moore Project Manager HNTB Corporation 111 N. Canal St. Suite 1250 Chicago, IL 60606 312-798-0290 William C. Thompson, P.E. Program Manager-CREATE Association of American Railroads 1501 S. Canal St. Chicago, IL 60607 Earl Wacker Director CTCO CSXT 1501 S. Canal St. Chicago, IL 60607 Robert Marros GIS Developer/Analyst HNTB Corporation 111 N. Canal St. Suite 1250 Chicago, IL 60606 Bruce Johnson Senior Designer HNTB Corporation 111 N. Canal St. Suite 1250 Chicago, IL 60606

ABSTRACT

LIDAR DATA ACQUISITION AND MAPPING FOR CHICAGO’S CREATE PROGRAM W. Robert Moore-HNTB William C. Thompson, P.E.-AAR Earl Wacker-CSXT Robert Marros-HNTB Bruce Johnson-HNTB CREATE, an acronym for the Chicago Region Environmental and Transportation Efficiency Program, seeks to modernize the freight and passenger rail infrastructure throughout the Chicagoland area. The program includes 66 defined projects in approximately 120 miles of rail corridors with capital costs totaling in excess of $1.5 billion. The projects include new signaling and tracks to enable the Class 1 and terminal railroads to interchange cars and operate more efficiently through Chicago, a series of flyovers to provide grade separation between Metra commuter/Amtrak Intercity service and freight operations, and 25 new roadway grade separations. The CREATE program is a public/private partnership including the freight railroads, Metra and Amtrak, through the Association of American Railroads (AAR), the Chicago Department of Transportation and the Illinois Department of Transportation to address the growing congestion in the nation’s rail hub. The AAR contracted with HNTB to provide engineering base mapping to support the program using modern Lidar (Light Detection and Ranging) and GPS (Global Positioning System) surveying techniques. HNTB’s subconsultants, John Chance Land Surveys, Inc. and Optram, Inc., provided aerial data acquisition and data extraction services, respectively. HNTB was the project manager and was responsible for land survey activities, data management, conceptual alignment design, mapping quality and accuracy verification and plan production. The AAR’s mapping project was the lead activity in the program to prepare Chicago to meet the transportation challenges of the 21st century. As the Lidar surveying process is a relatively new technique for use in route surveying, the project team encountered a variety of challenges in producing an accurate product, suitable for use by the project design consultants. HNTB offered a quality control strategy to quantify the mapping accuracy during its development. The stated objective was to achieve an absolute error not exceeding 4 inches (at one standard deviation) in the horizontal and 3 inches in vertical axes for most objects. The process has yielded GIS and CAD mapping products which are more accurate, timely and cost effective than those produced with comparable photogrammetric procedures.

This paper presents the data acquisition mapping objectives, procedures and results in a generally quantitative fashion. As the authors’ firms do not provide the mapping technology, the paper is intended to offer relatively unbiased recommendations and guidance to others who may be considering the use of Lidar technology for railroad design applications. Key Words: lidar data acquisition, base mapping, digital terrain model, FME workspace, control survey, GPS survey, Microstation, track geometry, Fli-Map, CREATE Program, Association of American Railroads, Chicago

Introduction

The Chicago Region Environmental and Transportation Efficiency Program (CREATE) seeks to modernize the freight and passenger rail infrastructure throughout the Chicagoland area, under a public/private partnership including the freight railroads through the Association of American Railroads (AAR), the Chicago Department of Transportation and the Illinois Department of Transportation. The railroads participating in the project include Amtrak, Belt Railway Company of Chicago, Burlington Northern and Santa Fe Railway Company, Canadian National Railway, Canadian Pacific Railway, CSX Transportation, Indiana Harbor Belt Railroad, Metra, Norfolk Southern Railway, and Union Pacific Railroad. This modernization is greatly needed to address the growing congestion in the nation’s rail hub. The projects include new signaling and tracks to enable the Class 1 and terminal railroads to interchange cars and operate more efficiently through Chicago, a series of flyovers to provide grade separation between Metra commuter/Amtrak Intercity service and freight operations, and 25 new roadway grade separations. The initial capital cost estimate prepared in mid-2003 is $1.5 billion. Federal SAFETEA-LU legislation enacted in August 2005 provides $100 million in the Projects of National and Regional Significance category, to initiate projects under the program. The federal funds will supplement funding provided by the railroads, the State of Illinois and the City of Chicago. In March 2004, the Association of American Railroads issued an RFP for the delivery of basemapping data for the CREATE program. The primary objective of the RFP was to obtain data to support the environmental review and engineering design of 41 railroad and 25 highway grade separation projects identified in the CREATE program. A secondary objective was to obtain GIS data for railroad assets for use by the railroads in managing and maintaining their infrastructure. The nominal corridor length of interest was 120 miles with corridor widths selected to cover the terrain approximately 100 ft on each side of the existing railroad track or planned new track centerlines. The AAR contracted with HNTB to provide engineering base mapping using modern Lidar (Light Detection and Ranging) and GPS (Global Positioning System) surveying techniques. HNTB’s subconsultants, John Chance Land Surveys, Inc. (JCLS) and Optram, Inc., provided aerial data acquisition and data extraction services, respectively. HNTB was the project manager and was responsible for land survey activities, data management, track geometry, property boundaries, data file format conversion, and mapping quality and accuracy verification. The Lidar surveying process was used successfully in the past for asset management of existing facilities, but is a relatively new technique for use in route surveying. As a result, the railroads sought to obtain assurance of the accuracy of the data collected, prior to distributing the mapping to individual project design consultants. HNTB offered a quality control strategy to quantify the mapping accuracy during its development. The stated objective was to achieve an absolute error not exceeding 3 inches at one standard deviation in horizontal and vertical axes for most objects.

The project used Chance’s Fli-Map (Fast Laser Imaging-Mapping and Profiling) lidar data acquisition system to collect data from a helicopter platform flying at 160 ft (50 m) and 260 ft (80 m) above the track centerline. Simultaneous digital still photography and video were captured to aid in the planimetric data extraction. This data acquisition process produced accurate digital elevation models and contour maps due to good foliage penetration, highly accurate laser measurements and precise positioning of the helicopter. The resultant mapping products are believed to be more accurate, timely and cost effective than those produced with comparable photogrammetric procedures. Chance’s Fli-Map Data Processing System, “FLIP7”, was used to process the lidar and image data. The data extraction process used both automated and manual procedures to identify and attribute planimetric features for use by the railroads in GIS applications and for use by engineers in designing the new infrastructure. The railroad and consultant team developed data management procedures to produce a variety of useful mapping products including georeferenced planimetric data, digital terrain models and CAD files in multiple formats. The digital mapping process yielded an enormous amount of data. Deliverables included raw lidar data, digital photography, video, project orthophoto mosaics, GIS and CAD products. Initially files were transferred among the design team staff on hard drives containing hundreds of gigabytes. To facilitate later data transfer, the Railroad GIS personnel intend to develop a web based map portal to allow CREATE stakeholders to access and down load current data.

The CREATE Program

While the railroad community has known for years that the Chicago terminal can be a bottleneck to efficient train movements, little progress was made in relieving the condition, prior to the late 1990s with the establishment of the Chicago Transportation Coordination Office and the implementation of a comprehensive computer simulation of railroad operations. The unprecedented cooperation among the nation’s independent railroad corporations and the development of a simulation program which quantified specific infrastructure improvement impacts allowed the railroads to recognize the mutual benefits of track and signal improvements on each other’s property in the densely crowded Chicago terminal area. Over the same period, the general public and political leadership at local, state and national levels have come to recognize that significant public benefits accrue from an efficiently operating freight rail system. The CREATE program provides railroad track and signal improvements within the Chicago terminal area, focusing on five major corridors over approximately 120 miles through the region. The corridors are presented in Exhibit 1. In addition, the project provides for highway grade separations at 25 sites, which allow the railroads to hold trains in close proximity to their destinations, minimizing delays in moving trains through busy track segments. Similarly, the grade separations save time for motorists, particularly in rush hours by eliminating conflict with train traffic. The general public

benefits as well, from improved Metra service on the Southwest Service and Heritage Corridors, where commuter travel times will be reduced and reliability improved with the construction of six flyovers and dedicated passenger service tracks. Some of the program’s benefits to the Chicago region and entire nation include a reduction in air pollution caused by idling automobiles and locomotives, an improvement in through travel times and a reduction in interchange delays through the nation’s principal railroad interchange terminal.

The RFP and Contract Requirements

In October 2003, the CREATE Program Railroad Engineering Committee presented the Illinois railroad transportation consulting community with a challenging request for information on the general subject of aerial photography and surveying. The committee was seeking input to aid in the development of an RFP for the base mapping project. Committee members expressed a keen interest in new technologies and their potential to offer highly accurate and cost effective products. Each firm was provided a brief, 15-30 minute period in which to discuss its views and experience. These meetings served to clarify the benefits to the railroads of using the new lidar technology. The benefits include enhanced personnel safety and reduced train delays due to a reduction in ground survey requirements, which can require track occupancy. In addition, the lidar data acquisition process provided significant cost savings in comparison to conventional survey techniques, plus high quality video and photography for use in design and environmental studies. Following these sessions, the CREATE staff formed a GIS Committee, comprised of qualified professionals, representing the major railroads, to develop the RFP document for mapping services. The Committee issued an RFP through the AAR in early 2004. Some of the key requirements and tasks of the RFP and resultant contract are reproduced as follows:

• Plan coverage for approximately 187 miles of flight paths • Verify quality and consistency of data across all project sites • Deliver data in GIS and a variety of CAD formats • Extract man-made and natural topographic features listed in the comprehensive

RFP Appendix D • Obtain appropriate permitting from the participating railroads to access railroad

property • Plot property boundaries based on val maps provided by the railroads • Establish ground control to support engineering and construction in the vicinity of

18 construction projects • Acquire aerial data and imagery in leaf off condition • Provide field verification of mapping accuracy • Conform to IDOT CAD standards • Develop existing track geometry

• Provide delivered data accuracy of 3 inches absolute for elevation and horizontal Required deliverable products included the following components:

• Raw aerial data: lidar, digital still photos, video • Planimetric data in CAD and GIS formats • Digital terrain models • Track geometry files • Orthophoto mosaic images • Survey data • FME Workspace and Data Dictionary for data extraction and conversion

The data accuracy requirement bears some explanation. HNTB and its subs determined that the initial specification requirement of 3 inch absolute accuracy was unlikely to be achieved for all extracted features, given the state of the art of aerial data acquisition using lidar and high resolution digital photography. The HNTB proposal and contract offered the following revised language: For extracted planimetric features and digital terrain models: “The absolute accuracy of discrete laser points (representing ground and feature data points) with FLI-MAP using OTF kinematic positioning is 6 inches horizontally and 4 inches vertically. The relative accuracy of data points collected with subsequent scans and to the kinematic control network is 4 inches horizontally and 3 inches vertically. The relative accuracy of points common to a single scan is 2 inches. These stated accuracies are to the 2 sigma confidence level (95% of data points will fall within this accuracy tolerance). FLI-MAP can achieve the accuracy specifications called for in the RFP (3 inches vertical and 3 inches horizontal) if FLI-MAP accuracies are stated at the 1sigma confidence level (67% of data points will fall within this accuracy tolerance) or Root Mean Square Error (RMSE). (Note: This was changed slightly to allow 4 inches horizontal accuracy during the project implementation based on observed data.) Elements that are small in size and have very few laser hits are more difficult to position in the X-Y plane due to the relative scarcity of data in the vicinity of the edge of the object. The data extraction process, supplemented with an examination of the digital photography will result in the achievement of horizontal accuracies of within 1 ft at the 1sigma confidence interval as presented in the planimetric mapping. Vertical accuracies on all but the smallest objects are unaffected. For imagery: The FLI-MAP system will provide digital video of the ROW, both forward (45 degree) and downward (90 degree) perspectives, in MPEG1, high bit rate format (320 x 240 pixels). Additionally, high-resolution digital still images (1280 x 960 pixels), taken in the forward and down perspectives once per second will be captured. FLI-MAP imagery will be used in conjunction with the LiDAR data to produce digital orthophotos of the survey corridor. These digital orthophotos cover an area approximately 85% of the width of the laser data. Digital orthophotos will be produced with a ground resolution of 0.25 feet per pixel and an accuracy of 1 foot.”

The Association of American Railroads issued a contract to HNTB on May 3, 2004 to begin work.

The Plan HNTB developed a plan to implement the project according to a relatively simple arrow diagram, prepared by the project manager, a fellow well schooled in one engineering

firm’s dictum, “plan the work and execute the plan”. Unfortunately, the diagram in no way revealed the complexity of the challenges ahead. The basic strategy is presented in Exhibit 2. The diagram depicts a sequential path to acquire and process aerial data to yield the various mapping deliverables. It is important to note that several activities occurred in advance of the flights, the most significant of which was flight planning, discussed in some detail below. The team devised two strategies for verification of mapping data accuracy, one of which required the setting of “lidar test point” targets in the railroad right of way in advance of the flights. The lidar test point process is also described within this paper. A particularly challenging activity was that of obtaining right of entry for the various ground survey activities required for accuracy verification and for setting ground control for later use in design and construction. Each of the railroads has its own requirements for access, insurance and flagging protection. Fulfillment of railroad specific applications, insurance certificates and track safety training were necessary prior to entering railroad owned property. In some cases flag protection was required while on the property. HNTB had to satisfy such requirements for UPRR, BNSF, CSX, NS, CPR, BRC, IHB, and Metra. The differing requirements among the railroads reflect to some extent different operating conditions, but perhaps more importantly the fact that each railroad has its own identity and business practices. Completion of the requirements and documentation took multiple weeks and a lot of correspondence. The consultant team recognized that obtaining the flight data in the maze of railroad tracks through the Chicagoland area would not be easy. The RFP included a preliminary flight description and project site listing including a series of historic rail line and subdivision names. From the air, identifying the proper rail line segment and project site limits can be quite a challenge. Fortunately, the railroad project staff joined the consultant team to plot flight paths, correlate flights and projects in a master spread sheet and verify that the flights had obtained sufficient coverage to map the project sites. Railroad staff flew in every helicopter flight and inspected the video records each night to confirm complete data acquisition. An examples of the flight planning activities is shown in Exhibit 3. The flight limits were plotted by hand on USGS maps, enabling the most knowledgeable railroad staff to review the planned data acquisition limits. The tabulated flight records ensured that data was collected and verified for each project. Individual railroad engineers or operations personnel were assigned the responsibility for projects on their property. The responsibility included an inspection of the planned flight paths and review of the flight videos to verify that the data acquisition had covered the required project limits. A war room was established at a centrally located hotel for flight planning and daily data review during the two week flight data acquisition period. The master spreadsheet, a segment of which is presented in Exhibit 4, survives to this day, as a record of the organizational effort necessary to make this happen and as a cross reference to correlate flights (which are date and time stamped) with project sites, which

are geographically referenced. The value of this thorough flight planning effort may be measured in the very few cases where later engineering studies determined that sufficient data had not been captured during the flight. The flights took place over a two week period in late May and extended through the Memorial Day weekend. As can be anticipated for spring in Chicago, the weather was not always cooperative. Periods of rain and blustery winds scrapped several planned flight days. The lidar process does not work as effectively in heavy precipitation and the helicopter pilot has great difficulty in flying the prescribed flight path and speed in the turbulent air close to the surface of the ground amid the numerous obstructions found in an urban setting. The challenging flight conditions may be envisioned in Exhibit 5. The pilot was always accompanied by a railroader, who possessed particularly good knowledge of the routes and no small measure of courage. In addition, it may be observed that the relatively late spring flights had allowed significant foliage growth, a problem explored in greater detail within this paper. One of the key benefits of the lidar data acquisition process is that some of the products are available almost immediately after completion of the flights and the initial post processing performed by JCLS. Initial products include lidar data, video, digital still photographs, and survey control (survey data for flight control), followed shortly by geographically referenced orthophoto mosaic images. Relatively minimal post processing labor is required to construct the orthophoto mosaics. However, the data extraction of planimetric features and development of digital terrain models is significantly more time consuming. Similarly, the digitizing and registration of the valuation map property boundaries, development of track geometry and assembly of the various file formats takes some manual effort. HNTB implemented a process to verify the accuracy of extracted surfaces and planimetric features, by simply comparing the coordinates and boundaries of a sample of the features to survey data obtained by conventional methods. This quality control process was implemented prior to delivery of the GIS and CAD products and is explained in greater detail in this paper.

Fli-Map and Flip 7 The project used Chance’s Fli-Map lidar data acquisition system to collect data from a helicopter platform flying at 160 ft (50 m) and 260 ft (80 m) above the track centerline. Two laser scanning systems operated at 50 Hz, collecting 200 ranges per scan or 10,000 ranges per second per laser. The low altitude of the helicopter and relatively low forward speed of 30-60 feet per second provided a nominal point density of approximately 25 ranges per square meter. (However, as is explained in this paper, the point density varies significantly with distance from the flight line, which impacts the accuracy of the data extraction of some features.) The helicopter position was established by post processing using helicopter inertial navigation system data and the GPS pseudo range and carrier phase data, collected during

the flight at a network of base stations set on NGS monuments along the route. Simultaneous digital still photography and video were captured to aid in the planimetric data extraction. The Fli-Map concept is illustrated in Exhibit 6. Chance’s Flip 7 software was used to post process the data. The software calculates the XYZ coordinates of the laser returns from GPS, platform attitude and laser data. In addition, Flip 7 allows the user to coordinate the video and still imagery to aid in extracting planimetric features from the lidar data. Flip 7 provides a variety of CAD functions, enabling the user to construct points, polylines and polygons. The extracted features carry the original point data including horizontal position, elevation and time. Flip 7 is also used to assemble 3 inch pixel resolution mosaiced orthophotos to enable viewing a larger area within a single file. By definition, an orthophoto is a digitally modified pictorial depiction of the terrain derived from aerial photography in such a way that there are no relief or tilt displacements. An orthophoto is equivalent to a planimetric, constant scale map except that the information is conveyed by different color and tone. (Ref. 1) The orthophotos do not always line up precisely with the extracted features due to a variety of distortions introduced in the photographic and digital rectification process. Chance noted that the error can be a great as 1 ft in the horizontal plane. However, we observed that the quality was generally very good as illustrated in Exhibit 7, which depicts topographic features displayed in Microstation with the orthophoto referenced using Raster Manger. The individual rails are displayed as an extracted line feature and appear to lie almost precisely on the photo background. A very slight distortion is evident in the lower right hand corner of the screen. The data extraction process used both automated and manual procedures to identify and attribute planimetric features for use by the railroads in GIS applications and for use by engineers in designing the new infrastructure. The principal strategy for reducing the original, large data set to a manageable collection of points is to use filters. Chance has developed a number of filters that facilitate the extraction of features like rails and power lines. In addition, the data extraction subconsultant, Optram, developed procedures to extract challenging features, such as points of switch and points of frog, which do not show up clearly in the laser data. Filters also facilitate the development of surface elevation models, as laser strikes from above ground objects such as structures and foliage must be eliminated to find the true ground surface.

Field Survey Activities

The CREATE program includes 66 defined projects within approximately 120 miles of railroad corridors. The AAR identified a total of 18 high priority projects for thorough data extraction to support design activities. These 18 projects were designated in Appendix A-3 of the AAR RFP, becoming the “A-3 Projects.” The contract required that HNTB set ground control to support further design and construction activities at roughly one mile intervals within the designated A-3 project

sites. The ground control points were set in working pairs and their coordinates established using static GPS procedures to achieve NGS Second Order, Class I accuracy, in accord with IDOT’s Bureau of Design and Environment Survey Manual, dated May 2001. The Lidar mapping process collected laser data from a helicopter, whose position was established by post processing using helicopter inertial navigation system and GPS receiver data and the GPS pseudo range and carrier phase data, collected during the flight at a network of base stations set on NGS monuments along the route. HNTB’s A-3 project ground control points were established using the same control network. Ground control was set over a period of time from May to August 2004. The ground control monuments were positioned according to the following criteria:

• Set in pairs at nominal spacing between points of 1200+ ft • Set at maximum intervals of one mile between pairs • Paired points must be inter-visible without frequent interference by trucks or

railcars • Sites unlikely to be disturbed by automotive traffic • Sites unlikely to be disturbed by construction • Sited at a sufficient distance from rail centerlines, so as not to require flagging

(nominally 25 ft) • Clear of shallow underground utilities as Feno monuments penetrate up to 4 ft • Accessible by automobile • Sited on railroad owned property

Due to geometric constraints and the presence of infrastructure elements and foliage, the criteria were not met in all cases. However, a best effort was made to set ground control that proves useful to future design and construction surveyors. A total of 73 points were established. The ground control monuments were generally comprised of a driven Feno rod and anchor with a brass cap labeled, “CREATE”, and included a stamped identification number. Occasionally, the brass cap was set in a preexisting, massive concrete element. In other cases, the site was marked by a cross cut in concrete. The specific monument type was described in the respective control point data sheet. The ground control, as well as the mapping products, was presented in the US State Plane 1983 Illinois East 1201 coordinate system. The horizontal project datum was NAD 1983. The vertical datum was NAVD 88. All units were in US survey feet. Ground Control Points were described in summary sheets and individual data sheets, which included coordinate data and descriptive information to aid in locating the points in the field. Each CREATE A-3 project submittal included a Microstation file, providing the locations of the ground control points for use in design plans.

GPS data was collected at the control points in multiple static occupancies using Trimble 5700 and 5800 equipment. HNTB used Trimble Geomatics Office software to perform the processing of the GPS data and the survey network adjustment. HNTB established six control networks, each tied to the original JCLS adjusted network. An adjustment report for each network was provided, including a network diagram and computations of horizontal and vertical error between observations. In all cases, the maximum horizontal error is well within Second Order Class I standards of 1:50,000.

Accuracy of Extracted Planimetric Features

As both the aerial data and survey control points were tied to a common adjusted NGS monument network, it was expected that conventional field survey data and aerial survey data would be very closely correlated. HNTB conducted a series of quality control surveys from the ground control using total station equipment. The comparison of field survey data and mapping data indicated that key features were accurately depicted in the mapping, with horizontal errors within 0.33 ft (4 in) and vertical errors within of 0.25 ft (3 in) for 67% of the data (one standard deviation). This analysis presumed that the conventional survey data was precisely accurate with no error. Typical comparisons between the survey and mapped data are presented in Exhibit 8.

Many of the key features have been plotted with what has historically been unachievable accuracy for aerial mapping. Four examples are explored as follows.

Rails and Track Centerlines:

As noted previously, rails and track centerlines may be extracted with very high accuracy due to the relatively automated extraction technique using a filter developed by John Chance Land Surveys. The data extraction procedure automatically filtered out, all but the laser strikes on the railhead. The resultant points were manually cleaned to eliminate apparent outliers and a polyline was fitted to the data using a least squares procedure. The two track polylines were averaged to provide a track center polyline with horizontal and vertical data at calculated vertices. Using Geopak, HNTB fit a track alignment string to the extracted centerline vertices. The horizontal fit employed tangents curves and spirals placed by a skilled designer. The profile used simple line segments connecting the extracted vertices.

HNTB surveyed track cross sections at a large number of sites in the A-3 projects using total station equipment and the ground control established for the projects. The survey crew obtained top of rail shots, which were averaged by the designer to construct new cogo points, whose coordinates were compared to those of the fitted track geometry at the point of intersection of the cross section. As a result, HNTB was able to compare the horizontal position of the track centerline as depicted in the coordinate geometry to that of the survey. A typical comparison is depicted graphically in Exhibit 9. Comparative data is presented in Exhibit 8. In virtually all sample cases, the difference in horizontal and vertical positions as determined by the two methods is well within the 0.33 ft (4 in) horizontal and 0.25 ft (3 in) vertical criteria. In the case depicted, for points O1866 and

H1866, the horizontal difference is 0.06 ft and the elevation difference is 0.09 ft. This is typical for tracks, which generally lie almost directly under the helicopter flight path with high lidar density and few lidar shots on the side of the rail head.

Points of Frog and Points of Switch:

Initial quality control comparisons of surveyed and extracted features revealed generally good correlation of the coordinates for most features. One surprising discrepancy was observed in the positioning of important track features including points of switch (POS) and ½ inch points of frog (POF). Errors ranged from 1.0 ft to in excess of 3.0 ft, leading the team to undertake a close examination of the problem. Generally, we found that the errors were greater for the POF, as the data extractor could see the long switch ties associated with the POS, aiding in the extraction process, but could not see the defining features of the POF as easily. As is illustrated in Exhibit 10, there is relatively little laser data in the vicinity of the point of frog, making an automatic and accurate extraction of this key feature very difficult.

The railroads were keenly interested in accurately locating the POS and POF as the determination of these points is necessary to establish accurate track geometry and to design the track changes required under the CREATE program. Most readers will be familiar with the point of switch, which is where the movable points of a split switch mate against the stock rail. The frog is the component that allows the flanged wheels to cross over opposing rails. The point of frog is found at the theoretical intersection of the two gauge lines. The ½ inch point of frog is established where the knife edge point is ground off at a ½ inch width between the gauge lines. Fortunately, most of the mainline turnouts employ lateral turnouts of relatively standard frog numbers on tangent track, facilitating the determination of the frog number and the point of intersection of the turnout.

The consultant team evaluated a number of procedures to position the POF and POS accurately. One option developed by Chance included examining the lidar data with the fused low altitude firewire imagery in an attempt to pick out the point of interest by examining all the visual clues including the presence of long switch ties, switch stands and guard rails. This procedure yielded better results. On a sample of 30 turnouts, Chance was able to pick the POS with an average error of 0.28 ft with 57% of the data within 0.25 ft, 93% within 0.5 ft and 97% within 1.0 ft. The accuracy for picking the POF was not as good with an average error of 0.57 ft with 28% of the data within 0.25 ft, 59% within 0.5 ft and 84% within 1.0 ft.

Another option included the use of templates based on standard turnout dimensions obtained from the railroads. This procedure yielded results similar to those of the visual procedure, but we discovered that the industry has developed a variety of dimensions for standard turnouts. The actual lead length (distance from POS to POF) varies considerably for typical turnouts used in railroad service. For example, the lead length for an AREMA #15 turnout is 126 ft 4.5 in, while the Metra standard is 111 ft 8.63 in. Similar variability is found among the railroads for other common turnouts.

Optram developed a graphical technique that resulted in accuracy meeting the specification requirements under the conditions of dense lidar data, good track conditions and clear photographs. The procedure relies on using the low altitude firewire imagery and a graphical process to determine the gauge line intercepts at the half inch point of frog. On a sample of 33 turnouts, HNTB measured Optram results as follows:

• POS Average Error of 0.18 ft with 74% within 0.333 ft, 90% within 0.50 ft, 98% within 1.0 ft.

• POF Average Error of: 0.22 ft with 63% within 0.333 ft, 77% within 0.50 ft, 93% within 1.0 ft.

• Combined POS and POF: 71% of the data falls within the specified 0.333 ft limits On the basis of these tests, the AAR and the consultant team determined that the technology had reached its accuracy limit. The specification was set accordingly. The results of this test are depicted in Exhibit 11.

Point Features and Small Objects:

Not all extracted planimetric features could be captured with such accuracy. Point objects (poles and signposts) surface objects (manholes, catch basins), small buildings and structures, bridge girders, curb lines are all difficult to extract to this level of accuracy with the currently available technology. The usual data extraction process includes filtering the lidar data to eliminate unwanted data points to aid in extracting the features or surface of interest. For example, the user may seek to eliminate lidar data that strikes the tree canopy, by eliminating laser data that is 10 ft above the ground surface.

The process of extracting features includes significant manual activities, which are subject to some level of error. Based on comparisons of survey and extracted feature coordinates, we believe that the specification accuracy of 1 ft (at one standard deviation) for small objects is a reasonable limit. An examination of the lidar data and feature extraction process serves to illustrate the challenge.

A key factor in accurate data extraction may be the lidar data density. The laser beam is triggered at a rate of 10,000 cycles per second and scans across the surface of the earth below the helicopter in a roughly 60 degree arc as directed by a mirror rotating at a constant speed. It is intuitive that the distance between laser strikes (perpendicular to the flight path) increases from most dense directly under the helicopter to less dense at the edges of the scan. The lidar data density can be calculated based on the geometry of the data acquisition configuration, but can also be demonstrated graphically as is shown in Exhibits 12 and 13. HNTB determined that the intensity varies by a factor of approximately 4:1, comparing the laser intensity under the flight path to that 120 ft away from the flight path for the low altitude flight.

Does the data intensity affect the accuracy of the data extraction? With some knowledge of basic physics, we can conclude that the quality of the measurement of time from laser trigger to laser return is very similar for beams directly

under the helicopter to those at a 120 ft offset. Some greater error may creep in due to the uncertainty of the helicopter inertial navigation system data for pitch, roll and yaw measured over a longer laser path. However, it is almost certain that the greatest component of the error lies in the human interpretation of the available data. Simply stated, the data extraction technician does much better work in areas directly under the flight path where he has dense data to define the outline of the extracted feature. His performance on the edges of the data is likely to be somewhat degraded. While we do not have a well developed analysis grouping the observed accuracy as a function of distance from the flight path, we conducted a simple experiment to test the theory. Two HNTB data extraction technicians performed data extractions on a small quantity of poles located approximately 80 ft from the flight line with primary laser data densities of 15 to 24 strikes per square meter. We discovered a very slight difference in the coordinates of points set by each individual as tabulated in Exhibit 17. The differences between the two measurements appear to be closely correlated to the data density with the greatest differences occurring where fewer strikes on the pole occurred. It should be noted that the lidar derived coordinates and surveyed coordinates for poles did not match with the accuracy observed for rail features. We attempted to understand why we observed relatively consistent error values and determined that the errors may be due to two factors. In some cases, we believe that the survey team simply measured the coordinates on the face of the pole and did not add the offset to the pole center consistently. Similarly, we believe that the data extractor spotted the center of the pole within the center of his data cluster. As the lidar data only paints one half of the pole surface, the 180 arc on the helicopter side, the data extractor could easily be positioning the pole center point with a consistent bias of one-half of the pole radius. However, the results appear to be within the permissible 1.0 ft error range at one standard deviation. While not conclusive, it is reasonable to speculate that the quality of the work in extracting small objects is a direct function of data density. This factor may be greater on low height objects such as fiber optic junction boxes and fence lines, where relatively fewer strikes would be observed in low lidar density areas out toward the edge of the right of way. In our comparisons of survey data for features such as fence lines, curb lines, junction boxes, fire hydrants, manholes, catch basins, (most of which are located out toward the property limits) we discovered that the surveyed and extracted coordinates did not correlate with the degree of accuracy observed in the rail data. Such features were considered small objects and subject to the lesser horizontal positional accuracy standard of 1.0 ft. Occasionally small features, such as utility markers, milepost signs, poles, culverts, and manholes may escape detection and not appear in the mapping, particularly where the color contrast and elevation difference are not great and the lidar data is not dense. It may be observed that the aerial survey firms, including Chance, are interested in developing greater laser scan rates in part to achieve more accurate results. Greater scan

rates could also allow more rapid data collection or collection from a greater elevation with accuracy similar to that obtained today.

Lidar Test Points The AAR divided the program in two mapping categories, so as to control the budget. The high priority projects defined the RFP Appendix A-3 included the extraction of a great variety of topographical features including railroad and streetscape elements. Lesser priority projects defined in the RFP Appendix A-1 included a reduced data set, limited to railroad features. In each case, the data acquisition process proceeded in an identical fashion. However, budget constraints limited the extent to which the mapping data accuracy could be verified. HNTB proposed a simple approach to test the process accuracy on roughly one mile intervals using “lidar test point” targets spaced at roughly one mile intervals for a total of 92 points. The targets consisted of 2.0 ft by 2.0 ft by 1.5 in white concrete patio stones placed in the railroad right-of-way. The coordinates of a corner of each stone were measured using GPS static procedures. Approximate positions and the corner quadrant were provided to the data extraction team for aid in locating the stones and providing a corresponding coordinates for comparison. (Note: finding the LTPs on the computer screen would have been easier if the stones were placed in areas of great color contrast!) This activity was performed very early in the data extraction phase and provided encouragement that the data extraction process would yield very accurate results. HNTB analyzed the results by resolving the data into northing, easting and elevation errors to yield a normal distribution provide for values with an expected mean of zero. We found that the average easting error was -0.03 ft with a standard deviation of 0.21 ft, the average northing error was 0.00 ft with a standard deviation of 0.25 ft and the average elevation error was 0.05 ft with a standard deviation of 0.17 ft. This is well within the permissible standard deviation of 1.0 ft horizontal and 0.25 ft vertical for small objects. A frequency distribution for all the data is presented in Exhibit 15.

Surface Models

Lidar data can produce extremely accurate digital terrain models. However, the process is not automatic and requires the judicious use of filters and good judgment on the part of the data extractor. If accurate surfaces are required as was the case in this contract with the 0.35 ft vertical accuracy criteria, the data extractor must take great care to filter out laser strikes on constructed features and vegetation. Similarly, the data extractor must use breaklines where the surface undergoes a sharp elevation change such as at curblines. An example of the problem is presented in Exhibit 16. As is discussed later, it can become extremely time consuming to construct accurate surface models under vegetation. Therefore we strongly recommend that flights be made in leaf off condition where terrain surfaces on normally vegetated railroad cut slopes and embankments are desired.

The team provided surface models in a variety of CAD configurations for use in virtually any CAD software.

Vegetation Effects Features that are not visible to the data extractor are generally not depicted in the planimetrics as they must be manually extracted. Such cases occur where the surface or feature is shielded by a structure (bridges and buildings) or where dense tree canopy obscures the view. In conventional aerial mapping, heavily vegetated areas are simply depicted as obscured because the stereoplotter provides no useable data on the surface below the vegetation. With lidar derived mapping, some of the lidar beams penetrate all but the densest canopy to provide data on the feature or surface below. In recognition of this fact, the data extraction team was directed to depict surfaces and features as accurately as possible under vegetation rather than void as is common in photogrammetric processes. The mapping products included a note that the user could expect to find a number of surface model errors in foliated areas, where the modeler was unable to see the ground surface. Under such conditions, it is very difficult to distinguish a laser strike on tree trunk, tree limb, retaining wall, or building or other man-made objects from that on a ground surface. The designer was urged to verify the surface under foliage, should he wish to construct infrastructure in the immediate vicinity. Similarly, the horizontal position accuracy can be degraded where foliage obscures the feature. Users were warned to expect to find missed features and inaccurate positions of marked features in areas of heavy tree canopy. Exhibit 17 provides a typical example of the problem in heavily foliated conditions.

CAD Products and FME Workspace

The AAR’s RFP included a Data Directory which identified the features to be extracted under the contract. This Data Dictionary was expanded during the implementation of the work to include feature layer names and attributes assigned by the data extraction team. AFeature Manipulation Engine (FME), developed by SAFE Software, was used to convert the extracted planimetric features derived from the lidar data, digitized property boundaries obtained from railroad valmaps and field survey data to Microstation/J v7 files for use by the various project design consultants preparing preliminary designs for the CREATE projects. The CAD submittals conformed to the current 2004 IDOT CAD standards in accord with the contract, although it was recognized that Microstation/J with its limited quantity of 63 levels does not allow full flexibility to segregate features. At the time the work was performed, IDOT was in the process of migrating to Microstation v8 and the FME supported only v7. The railroads had requested data in a variety of formats resulting in a relatively complex data flow sequence as illustrated in Exhibit 18. The mapping products were furnished on CD and DVD.

Conclusions and Recommendations The CREATE Mapping Project has proved a successful demonstration of modern aerial mapping technology. From the client’s perspective, the mapping products filled the defined engineering and GIS needs, very cost effectively and in a timely manner. The consultant’s quality control team has found the overall quality and volume of the work to be extraordinary. No new venture would be complete without a summary of lessons learned and some admonitions to those who would travel this path in the future. Some simple observations are as follows:

(1) Aerial lidar data acquisition and extraction technology offers a safe and cost effective means of obtaining surface feature and elevation data in the railroad environment without risk to personnel and the cost of disruption to normal train movements.

(2) Lidar technology makes it is possible to obtain extremely accurate aerial mapping. However, the accuracy of the data extraction is improved as more automated software extraction functions are developed. This warrants some further consideration on the part of the technology providers.

(3) While lidar data may indeed penetrate foliage, an enormous amount of labor is required to extract an accurate terrain surface and planimetric features beneath the tree canopy. Every effort should be made to fly the rail corridor in leaf off condition if features and surfaces outside the limits of the ballast are desired.

(4) The accuracy of the data extraction depends greatly on the density of the lidar data. Where very high positional accuracy is desired, low altitude flights with small offset to the features of interest are recommended. This requirement may change with the introduction of higher frequency laser scanning technology. (Hopefully computer processing and memory access speeds will increase accordingly.)

(5) Plan to call Julie (or the equivalent) when installing ground control monuments. To ensure that the sites are well clear of underground utilities.

(6) Some means of real time quantitative accuracy verification is useful, both to keep the extraction team on their toes and to discover any blunders. Be very careful in defining how extracted objects will be measured to ensure that the surveyors and the data extraction technicians are measuring the same thing. A meeting including photos and diagrams would be useful.

(7) Define the CAD standards carefully to ensure that specific features are segregated on multiple layers allowing some flexibility in using the data for engineering design. Avoid lumping all the railroad features on a single layer.

(8) Ensure that the data extraction team has a good knowledge of the physical features that will be extracted. Domain knowledge is key to accurate photo interpretation.

(9) Use breaklines liberally at all abrupt changes in surface elevation or slope.

(10) Include a culture check at some part in the mapping or early design process. Photo identification of some surface materials is difficult and no aerial data acquisition technology can extract features that are:

• obscured by vegetation or other structures • small and blend in with the background coloration • provide little vertical relief and blend in with the background coloration (11) Make adequate plans to manage the large volumes of digital data for daily

indexing during data acquisition and cataloging/storing the generated mapping products.

References:

(1) Surveying Theory and Practice, Anderson and Mikhail, 1998, page 773.

LIDAR DATA ACQUISITION AND MAPPING FOR CHICAGO’S CREATE PROGRAM

LIST OF EXHIBITS

Exhibit 1 CREATE Rail Corridors Exhibit 2 Initial Project Plan Exhibit 3 Flight Planning War Room Exhibit 4 Flight Documentation Exhibit 5 Flight Conditions Exhibit 6 Fli-Map System Concept Exhibit 7 Orthophoto Accuracy Exhibit 8 Comparison of Survey and Mapped Coordinates Exhibit 9 Track Centerline Comparison Exhibit 10 Lidar Beams on Point of Frog Exhibit 11 POS and POF Accuracy Analysis Exhibit 12 Lidar Beam Density under Flight Path Exhibit 13 Lidar Beam Density 120 ft from Flight Path Exhibit 14 Picking Variability with Lidar Beam Density Exhibit 15 Lidar Test Point Error Distribution Exhibit 16 Effects of Foliage on Surface Model Exhibit 17 Breaklines for Accurate Surface Models Exhibit 18 Data Flow Diagram

Exhibit 1: The CREATE Program defined five rail corridors and approximately 25 highway grade separations in the greater Chicagoland area.

CREATE BASE MAPPINGCONTRACT PROGRAM

ObtainRight of Entry

FlightPlanning

Aerial Data

AcquisitionVideo

OrthophotoLidar Data

Survey Control

DataReduction

ROWVerification

GIS/CAD:DTM, Contour

MapPlanimetrics

ROWTrack Geometry

Ground Control

Cross Section Survey

Set LidarTest Points

Create Map Portal (option)

Exhibit 2: Initial Project Plan to Acquire Data and Develop Mapping Products

Exhibit 3: Railroad and consultant staff planned and reviewed the data acquisition flights in a war room set up for the duration of the flight stage.

Exhibit 4: Flights were documented in a spread sheet developed by the railroads.

Exhibit 5: The challenging flight conditions are evident in the rain squall in the distance and close proximity of the power lines.

Exhibit 6: Chance’s Fli-Map system concept

Exhibit 7: Three inch pixel resolution orthophoto displayed in Microstation v8

Exhibit 8: Comparison of surveyed and mapped data with horizontal and elevation differences measured in feet

Exhibit 9: Graphical comparison of extracted and surveyed track centerline

Exhibit 10: Relatively few lidar beams strike the frog, making an automated extractithe POF very difficult (both lasers 160 ft flight elevation)

on of

• • •

Exhibit 11: Point of switch and point of frog accuracy analysis

Exhibit 12: Lidar beam strikes under the flight path for 160 ft flight elevation

Exhibit 13 Lidar beam strikes at 120 ft from the flight path for 160 ft flight elevation

Exhibit 14: y be observed in picking point objects in reas of lower lidar density

Greater deviations between picks maa

Exhibit 15:

Frequency distribution for Lidar Test Point coordinate errors

Exhibit 16: Breaklines are required where the slope of the surface changes rapidly to

btain accurate surface elevations o

Exhibit 17: Heavy foliage can obscure manmade objects resulting in inaccurate surfacmodels

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23+40

23+40

23+60

23+60

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.GPK

.DGN

.SHP

Features (ASCII)

(No Ground Points)

True Ground Points (ASCII)

Flip7•Process Rail•Process Non-Rail (A3)•Process Breaklines

Filter & Sur (A3)

•Ground face Clean (A3)•QA Check•Additional Fields (documentation)

SQL DB•Unique ID •Statistical QA Check•Databasing

ASCII

ArcGIS•Convert to Shape Files•Export to Terramodel•QA Check

CivilPak/GeoPak

•CoGo•QA Check

(3D Centerline Vertices)

HNTB DeliverablesShapefiles (plaimetrics, property, survey)Topo File (DGN v7, DGN v8)Surface Model (.tin, .dtm, .dat)Track Geometry (.gpk, .alg, .xml)Orthophotos & Wireframes (.tif, .dxf)Metadata (.html, .xml)Text (.doc)

Terramodel•Create DTM Model from Ground and Breaklines.•QA Check•Export DGN

OPTRAM Planimetric Feature Deliverable

Shapefiles (includes documentation

fields)

FME

OPTRAM Gr Model oundProject De verableli

GeoPak TIN

CREATE Data WorkflowRevised 12/03/04

.DG

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ArcGISROW boundary development

and Theoretical MP

ME

TAD

ATA

Field Survey•Lidar Test Points•Ground Control•Test Cross-sections

GeoPak1 ft contours

.DG

N &

.SH

P

.SHP .SHP

.SH

P

.TIN

.DG

N

Work TaskOptramHNTB Corp.

.XML

.HTML

Milepost

•Extract Physical MP location from ROW and LiDAR

Exhibit 18: Data Flow Diagram