Research Project:

Application of Advanced Remote Sensing Technology to Asset Management 

Principal Investigator | External Project Contact | Project Objective | Project Abstract | Task Descriptions | Milestones, Dates | Student Involvement | Relationship to Other Projects | Technology Transfer Activities | Potential Benefits of the Project | Budget | TRB Keywords

Final Report

This project yielded three separate reports:

• Grade and Cross Slope Estimation from LIDAR-based Surface Models
  • PDF version 1.9 mb
  • HTML version 183k
• Use of LIDAR-based Elevation Data for Highway Drainage Analysis: A Qualitative Assessment
  • PDF version 9.5 mb
  • HTML version 126k
• Remote Sensing (LIDAR) for Management of Highway Assets for Safety
  • PDF version 1.4 mb
  • HTML version 181k

Principal Investigator

Shauna Hallmark
Iowa State University
(515) 294-5249
shallmar@iastate.edu

External Project Contact

Dave Plazak
Iowa State University
(515) 296-0814
dplazak@iastate.edu

Project Objective

To evaluate and demonstrate the use of advanced airborne remote sensing techniques to locate, describe, and monitor transportation assets for improving inventorying, maintenance, and management. 

Project Abstract

LIDAR technology integrated with airborne GPS and inertial measuring can allow surface measurements (x,y,z) with a typical vertical accuracy of 15cm. Further data processing can extract measurements of bare ground (removal of vegetation, snow cover, etc.); also, digital aerial photography can be obtained as LIDAR measurements are taken. For this project, LIDAR data will be collected for two pilot study areas, assessed for accuracy and applicability relative to asset management data needs, and applied to three applications to evaluate practical use of the technology.

Task Descriptions

1. Obtain (existing) LIDAR dataset from Iowa DOT for first study area
2. Select second pilot study location
3. Perform LIDAR reconnaissance of second study area
4. Evaluate spatial accuracy of LIDAR dataset (i.e. compare to traditional data).
5. Verify LIDAR to ground truth for elements such as roadway width and grade.
6. Document existing performance assessment methods for pavements and bridges.
7. Identify factors related to pavement and bridge deterioration.
8. Assess feasibility of using LIDAR to identify as-built, ROW, and other features.
9. Develop methodology to extract features from remotely sensed data.
10. Develop methodology to include new data into performance assessment models.
11. Identify crash types of older drivers (70 and older).
12. Identify underlying accident causes that can be attributed to roadway characteristics.
13. Develop methodology to identify high-crash roadway features using remote sensing.
14. Develop method to rank and prioritize locations for safety improvements.
15. Evaluate cost effectiveness of remote sensing for identifying safety improvements.
16. Identify data needs to assess flooding.
17. Assess whether existing sources of topographic data meet needs for flood assessment.
18. Assess whether LIDAR terrain data meets flood assessment needs.
19. Evaluate whether IFSAR terrain data meets flood assessment needs.

Milestones, Dates 

Project Start: July 2001
Obtain LIDAR dataset: September 2001
Select second location: September 2001
Recon second area: September 2001
Evaluate LIDAR accuracy: March 2002
Verify to ground truth: March 2002
Document performance measures: September 2001
Identify deterioration factors: December 2001
Assess LIDAR to identify features: March 2002
Develop method to extract features: June 2002
Develop method to include new data: September 2002
Identify older driver crash types: December 2001
Identify attributing features: March 2002
Develop method to identify: September 2002
Develop method to rank: September 2002
Evaluate cost of using LIDAR: September 2002
Identify data needs to assess flooding: March 2002
Assess existing data sources: June 2002
Assess LIDAR: June 2002
Evaluate IFSAR: September 2002
Write Final Report: December 2002
Project End: December 2002

Student Involvement (e.g., Thesis, Assistantships, Paid Employment)

(1) Ph.D. Graduate Research Assistant
(3) M.S. Graduate Research Assistant

Relationship to Other Projects

The project is primarily focused on the "supply-side" of information exchange, in that it evaluates the parameters of using remote-sensing technology to provide relevant data for an array of asset management purposes. Conditioning LIDAR data to satisfy input requirements for managing transportation assets supports the more "demand-side" activities being undertaken, such applying input data to economic decision modeling, and integrating it into a conceptual framework for asset management. The studies supported herein will also provide focus and insight relative to extracting data from remote sensing activities.

Technology Transfer Activities

Distribution of the findings to participants involved in the study, publication and presentation at professional conferences, and possible workshops and other training activities.

Potential Benefits of the Project

LIDAR could potentially offer an entirely new method of data collection for participating agencies as well as an additional level for data to support inventorying, performance assessment, and decision-making for asset management systems. 

Budget

$256,377

TRB Keywords

LIDAR, remote sensing, technology, asset management, decision support