Guide for
Efcient Geospatial Data
Acquisition using LiDAR Surveying
Technology
Spring 2016
3D Engineered Models:
Schedule, Cost, and Post-Construction
An Every Day Counts Initiative
2
Contents
Introduction .................................................................................................... 1
Program Planning: Developing Information Requirements
...................... 2
Data Acquisition Technology .......................................................................................3
Data Requirements
......................................................................................................4
Derived Product Requirements
....................................................................................5
Data Management and Integration
...........................................................................7
Program Implementation: Enterprise LiDAR Surveys Data Acquisition .... 7
Developing a Business Plan ..........................................................................................7
Defining an Enterprise Data Governance Program
..................................................7
Updating Agency Survey Manual and Specifications
..............................................7
Establishing Data QC and QA Procedures
.................................................................8
Launching a LiDAR Survey Program: Agencies versus Professional Services
.........8
Consideration for Future Use of Geospatial 3D Data ................................ 9
1
Efcient Geospatial Data Acquisition
using LiDAR Surveying Technology
Introduction
State transportation agencies (STAs) rely on federal and state funding
to maintain and operate the transportation system within each state.
The use of federal funds for transportation projects is regulated by the
laws established by Congress. The Moving Ahead for Progress in the
21st Century (MAP-21) transportation bill introduced new programmatic
performance measures with which the STAs must comply to use federal
funds. MAP-21 has two major components: performance measures (i.e.
data about the use, condition, and impact of the transportation system)
and performance-based funding (i.e. performance measures to assist in
prioritizing and selecting projects for funding), thus focusing on promoting
transparency and accountability in the spending of public funds. Figure
1 summarizes the performance-based funding process. In order to
comply with these new rules, STAs are relying on implementing innovative
new processes and technology for managing roadway assets; LiDAR
1
surveys are one of these key enabling technologies.
Figure 1: Illustration of Performance-Based Funding
2
1 Light Detection And Ranging
2 http://www.cmap.illinois.gov/about/updates/-/asset_publisher/UIMfSLnFfMB6/content/map-21-
performance-measures-and-performance-based-funding. Accessed February 12, 2016
Efcient Geospatial Data Acquisition using LiDAR Surveying Technology
2
LiDAR surveys comprise an ever-evolving set
of technologies that allow for a rapid, yet
very accurate, collection of roadway asset
data through a single effort which, when
specified, integrated, and used correctly,
can result in efficient workflows for agencies.
While STAs may already be collecting data
for specific purposes, these efforts are often
duplicated within various agency disciplines
using a variety of collection methods and
standards. LiDAR surveys help consolidate
resources, thus maximizing funding and
enhancing the accuracy and integration of
information. Additionally, some traditional
survey practices could expose staff to
unsafe conditions and create unnecessary
traffic delays for the traveling public. Thanks
to LiDAR, agencies across the country are
beginning to conduct a variety of activities
across disciplines that can be completed
more rapidly and in a much safer and
collaborative environment.
While the process for implementing LiDAR
surveys will depend on a number of factors
— most notably the agency’s current
pre-construction, post-construction, asset
inventory practices, process maturity,
internal technical resources, and available
funding — this guide draws the decision
makers’ attention to the key issues that must
be addressed for the optimization of data
collection for use in an enterprise digital
data solution. These include developing
information requirements for data collection
and products to be delivered for specific
purposes and disciplines within the agency,
and implementing the data collection
program itself.
Program Planning: Developing
Information Requirements
Unified information requirements are the
foundation of a LiDAR program. They
represent what an agency needs to know
(i.e., to collect) in order to support the work
of its various disciplines. These disciplines,
however, tend to work with different
terminologies, domains, and applications. As
a result, information requirements within the
agency vary depending on who is going to
use the information and how. LiDAR surveys
provide a unique opportunity to consolidate
resources through a single collection effort.
In exchange, however, they require all
agency disciplines to define a common set
of information requirements (shown in Figure
2).
LiDAR helps to capture and collect vast datasets quickly, accurately and safely.
3
Sharing enterprise data can only be achieved if it meets the needs of all stakeholders
and if it is easily accessible. LiDAR surveys are a key means of beginning to overcome this
challenge. Therefore, before initiating the data collection process, all disciplines within any
given agency must collaboratively develop a unified set of information requirements that
will guide data collection so that it satisfies everyone’s operational needs. Such a set will
include, at minimum, products to support transportation planning, right-of-way acquisition,
environmental assessments and historic preservation, 3D design for roadway and structures,
construction workflows, traffic operations, signing and striping, highway safety, maintenance
activities, and multi-modal operations.
Efcient Geospatial Data Acquisition using Lidar Surveying Technology
Figure 2: Development of Information Requirements for LiDAR
Data Acquisition Technology
3
The technology to support data collection using LiDAR surveys is well established, but it
continues to evolve to incorporate new advances in hardware and software. Today’s data
collection systems incorporate a variety of sensors based on three (3) major platforms, as
shown in Figure 3. Availability of a GNSS network can be beneficial to an agency.
The purpose of the data determines the type of platform necessary for collecting it. Note
that additional sensors may be added to the mobile platform for collecting pavement
analysis, such as pavement profilers, and the Laser Crack Measurement System (LCMS
TM
).
4
Platform: Helicopter or fixed wing airplane
Sensors: IMU, GNSS, digital camera, laser scanner (LiDAR)
Hardware (computer) and software
Platform: vehicle (truck or van)
Sensors: IMU, GNSS, digital camera, laser scanner (LiDAR)
Optional Sensors: Pavement profilers, pavement analysis
Platform: Tripod
Sensors: GNSS, digital camera, laser scanner (LiDAR)
Hardware (computer) and software
3 Process of collecting data using remote sensing technologies, such as 3D surveys, photogrammetry, and satellites
4 Pavemetrics (www.pavementrics.com)
Figure 3: LiDAR Surveys Data Acquisition Platforms
4
What is LiDAR?
` LiDAR, Light Detection And Ranging, is a remote
sensing method that uses pulsed laser light to
examine terrain and generate precise, three-
dimensional (3D) information regarding surface
shape and characteristics.
` LiDAR allows transportation agencies to capture
and collect vast datasets more quickly,
accurately and safely when compared to
traditional survey methods.
Data Requirements
5
The resulting LiDAR survey product is a point
cloud from which many derived by-products
can be extracted and delivered to be
consumed for various applications. A point
cloud is a collection of data points in 3D space
(x, y, z positioning), and it is defined by its
characteristics, specifically: accuracy, density,
and intensity. When setting data collection
requirements, it is important to specify the
desired characteristics and the level of post-
processing to be completed on the final
product(s).
Accuracy, Density — Today, LiDAR surveying systems are categorized by asset/mapping
grade and survey/engineering grade. The distinction is significant given its applicability, thus
it is important for these requirements to be clearly communicated. Mapping grade accuracy
is cheaper but only acceptable for applications requiring accuracies within a couple of feet,
such as asset management and inventory mapping. On the contrary, survey/engineering
grade accuracy is needed for applications requiring inch and sub-inch accuracy, such as
engineering surveys or engineering design, and it is also much more expensive. In addition
to accuracy, the desired data density should also be clearly specified. The need for the
type of survey is driven by the information requirements set up front by the agency during
the planning process. Greater accuracy generally results in more costs for data collection,
processing, and storage. However, the data can be used beyond the intended purpose
because it was collected at the highest possible accuracy level. Sensors continue to
quickly advance to improve accuracy, thus performance-based requirements are highly
recommended.
Table 1: Point Cloud Requirements
Point Cloud Requirements Description of Point Cloud Requirements
Accuracy
Network – Value that represents the uncertainty in the coordinates of the control points used for
collecting the point cloud data with respect to a geodetic datum at 95% condence.
Absolute – The level of accuracy that can be obtained in a global coordinate system without
reference to a geodetic datum.
Local – Value that represents the uncertainty in the coordinates of the points in the point clouds
relative to each other at 95% condence level.
Density of
Resolution
The attribute that describes the number of points per unit area. It can also be expressed as the
average distance between points in a point cloud (e.g., 2-ft spacing).
5 This section is based on the recommendations of NCHRP Report 748 Guidelines for the Use of Mobile LiDAR in Transportation
Applications. For more detailed requirements, please refer to NCHRP Report 748.
5
Efcient Geospatial Data Acquisition using Lidar Surveying Technology
Table 2: Vertical Accuracies of LiDAR Data Acquisition Methods
6
Method Network Accuracy (RMS)
Fixed Wing Aerial LiDAR/Photogrammetry 3” - 6”
Low Altitude Helicopter LiDAR/Photogrammetry 1” - 2”
Mobile LiDAR ½” - 1”
Tripod-Mounted Static LiDAR ¼” - ½”
Post-Processing — This is the process for making a point cloud consumable by other
applications. It includes steps to extract data for a variety of applications (shown in Figure 4).
Geo-
Referencing
Tiling
Noise
Reduction
Classification
Data
Extraction
Quality
Control
Process of
assigning
coordinate
system and
location
information
Process for
creating
sections or
“tiles” for
easier
viewing and
manipulation
Process for
cleaning
artifacts from
the point
clouds and
filtering data
points
Process for
assigning a
point in a
single
predefined
category, such
as ground,
vegetation,
etc.
Process for
extracting
specific data
relevant to an
application
such as GIS
and CADD
Process for
ensuring point
cloud and
derived
products will
meet required
specifications
Geo-
Referencing
Tiling
Noise
Reduction
Classification
Data
Extraction
Quality
Control
Process of
assigning
coordinate
system and
location
information
Process for
creating
sections or
“tiles” for
easier
viewing and
manipulation
Process for
cleaning
artifacts from
the point
clouds and
filtering data
points
Process for
assigning a
point in a
single
predefined
category, such
as ground,
vegetation,
etc.
Process for
extracting
specific data
relevant to an
application
such as GIS
and CADD
Process for
ensuring point
cloud and
derived
products will
meet required
specifications
Figure 4: Basic Workflow for Post-Processing Point Clouds
Derived Product Requirements
Derived Products — Each application within a transportation agency has specific
requirements in terms of accuracy and product types necessary to perform a function.
Typical products extracted from a point cloud include features and metadata for databases
(e.g., GIS, LRS, asset inventory) and CADD data (e.g., 3D models)
7
, and if the 3D data
acquisition system is equipped with a camera, additional products may include media files
(e.g., imagery and videos). Figure 5 represents a sample of the types of products derived
from a geospatial 3D survey and some of the applications in a transportation agency.
Data Dictionaries — A data dictionary consolidates all the requirements necessary to
develop an adequate asset inventory. It details the assets to be collected, the features
to be recorded for each particular asset, and the descriptors or identifiers to be used
when recording each specific feature. Assets to be collected can be divided into two
major groups: longitudinal (pavement, shoulders, guardrail) and discrete assets (bridges,
sign structures, traffic signals). Then, for each asset, four different groups of features can
be collected. These include characterization (ID number, subtype), location (route,
6 FHWA EDC-3 3D Engineered Models: Schedule, Cost and Post-Construction Workshop Materials
Image credits: Wikimedia Commons; Table credit: Wisconsin DOT
7 GIS – Geographic Information Systems, LRS – Linear Referencing Systems, CADD – Computer Aided Drafting & Design
6
coordinates
8
), geometry (width, height), and condition. Lastly, each feature can be
recorded according to a specific descriptor, which includes a specific data type (e.g.,
number, text), data format (e.g., 3 digits, 6 digits, 10 characters), unit of measure (e.g., mile,
point, feet, meter), and range of values (e.g., negative and positive, left and right).
Figure 5: Typical Data Derived from 3D Survey Data Acquisition Systems and Examples of Applications
9
Data Formats, Storage, Accessibility, and Archiving — For data to be shared by many
systems and applications, the agency must require that data products be delivered in
compatible, industry-standard file formats. Table 3 provides some examples. In addition, a
thorough data management plan must be developed. Once the data is collected, it must
be stored either at the agency or externally via enterprise data storage or a hosted service in
such a way that it is easily accessible to the data consumers. A plan to back up and archive
data should also be developed.
10
Table 3: Sample Data Formats by Type of Data Product
Data Product
LiDAR Point
Cloud
Images Video CADD Data GIS Data Database
S a m p l e D a t a
Formats
LASer File For-
mat Exchange
(LAS), LAZ
10
, or
ASTM E57
Geographic
Tagged Image
File Format
(GeoTIFF) or
System Com-
patible File
Format
Audio Video
Interleave (avi),
MPEG-4 Video
File (mp4), Win-
dows Media
File (wmv), or
System Com-
patible File
Format
Digital Terrain
Models (dtm,
tin)
Drawings (dgn,
dwg)
Digital Eleva-
tion Models
(DEM)
Geodatabase
Shape File
(shp) or System
Compatible
File Format
Digital Eleva-
tion Models
(DEM)
Comma Sep-
arated Value
(csv) or System
Compatible
File Format
8 The agency will need to define the geodetic information (coordinate system, geodetic datum, etc.) with which they want to work.
9 Image credit: Iowa DOT, Utah DOT, Caltrans, Missouri DOT, and FHWA.
10 Compressed version of LAS (typically 10-20%) without data loss. (NCHRP Report 748 Guidelines for the Use of Mobile LiDAR in
Transportation Applications)
7
Efcient Geospatial Data Acquisition using Lidar Surveying Technology
Data Management and Integration
Data management and integration is a much larger task than acquiring the data, and it
requires a comprehensive enterprise data governance policy and strategic data integration
plan. The agency solution to manage, integrate, and disseminate data relies on information
systems infrastructure and technical support for the data to be accessible and to be of most
value. A representation of data flow from data collection to business decision is shown in
Figure 6.
Figure 6: Data Flow and Evolution from LiDAR Surveys to Business Decision
Program Implementation: Enterprise LiDAR Surveys Data Acquisition
Developing a Business Plan
Having a clear business plan for collecting and using geospatial 3D data is paramount for
a successful enterprise solution. At a minimum, a business plan should include a strategy
to bring the organization from current state to the desired solution, including setting the
organization’s vision and goals, identifying priorities and resources, establishing milestones
for specific tasks in the plan, and defining a timeline for accomplishing the overall goals.
A thorough assessment of the agency’s processes and maturity of infrastructure and
technology expertise should be conducted prior to developing the agency strategy.
Defining an Enterprise Data Governance Program
The purpose of an enterprise LiDAR data acquisition program is to leverage data from
collection through integration and dissemination. In order to accomplish this task effectively,
it is imperative to establish an enterprise data governance plan. This document does not
provide guidance on establishing a data governance program; that topic will be covered in
a separate document titled Guide to Efficient Geospatial 3D Data Integration.
Updating Agency Survey Manual and Specifications
A survey manual and specifications that outline modern practices in geomatics are both key
components in establishing a LiDAR survey data acquisition program. The survey manual and
specifications are the foundation for establishing the specific protocol for data collection,
including modern surveying techniques, desired products and related characteristics (such
as accuracy, density, and intensity), quality control (QC) and assurance (QA) procedures,
and acceptance protocol. In addition, these specifications need to be written in such a
way that the guidance itself becomes a living document that can evolve along with the
emerging technology and practices. Thus, establishing performance-based methods is
the best approach for these specifications. The responsibility to update or set up a survey
manual and specifications lies with the geomatics and surveying professionals, but it requires
input from all stakeholders.
8
Establishing Data QC and QA Procedures
The quality of data captured by a LiDAR survey will largely determine the quality of the
business decisions made from it. Thus, as collection takes place, the data should be subject
to thorough QA. However, given the large amount of data at hand (likely in the order of
tens of terabytes), QA cannot only occur at the end of the data collection exercise —
redundancy of checks in the QA process is paramount. Therefore, an agency should not
only designate a competent team to review and ensure that the deliverables meet the
requirements, but also consider securing the services of a qualified and independent team
to verify that the work product is compliant with the specifications.
The same considerations apply if an agency decides to outsource its LiDAR survey data
acquisition program. In such a case, however, the contractor should also be required to
provide a QC plan that explains how accurate and high-quality data will be achieved and
maintained and how any deficiencies will be remedied. To further encourage delivery of
high-quality data, incentive and disincentive clauses can be considered in the contract
letting process based on independent checks conducted at designated road sections.
11
Launching a LiDAR Survey Program: Agencies versus Professional Services
12
Agencies can either establish a LiDAR survey program internally or procure professional
services. The cost of agency ownership includes the cost of equipment, training, post-
processing software, and any associated maintenance and upgrades. Alternatively,
procuring professional services only includes the cost for data acquisition and required
products. A third option would be to consider a hybrid approach, in which the data
collection is procured while the post-processing is performed internally with agency
resources. The data acquisition workflow is shown in Figure 7.
Figure 7: LiDAR Survey Data Acquisition Workflow
11 Each check would involve a comparison between values measured by the agency and values measured by the contractor for a
particular feature. The overall potential for incentives/disincentives should be around 10% of the total contract value.
12 More detailed information may be obtained from NCHRP Report 748
9
Efcient Geospatial Data Acquisition using Lidar Surveying Technology
Consideration for Future Use of Geospatial 3D Data
Effective use of geospatial 3D data is in its infancy, but offers potential beyond our
imagination. Platforms will continue to evolve, accuracies will continue to improve, and
new software applications will emerge to accommodate current and future needs. While
implementing an enterprise LiDAR survey data acquisition program is a large task to
undertake, agencies can optimize their current data acquisition practices to grow and
evolve to rely on geospatially accurate data to manage roadway assets throughout their life
cycle.
Every Day Counts, a state-based initiative of the Federal Highway Administration’s Center for
Accelerating Innovation, works with state, local and private sector partners to encourage
the adoption of proven technologies and innovations to shorten and enhance project
delivery.
10
For additional information about this EDC Initiative, please contact:
Christopher Schneider
Construction Management Engineer
Office of Infrastructure (HIAP-30) — FHWA
Phone: (202) 493-0551
Email: [email protected]
R. David Unkefer, P.E.
Construction & Project Management Engineer
FHWA Resource Center - Atlanta
Phone: (404) 562-3669
Email: [email protected]
FHWA-HIF-16-010