A Framework for Network-Level Pavement Condition Assessment Using Remote Sensing Data Mining

Pavement condition monitoring is essential for efficient resource allocation in transportation asset management. However, the collection of data involves laborious and costly procedures. The intention of this study is to investigate the usage of remote sensing data for network level pavement condition assessment in order to provide a more cost-effective alternative. For this reason, an extensive literature review has been conducted and a data mining framework has been established utilizing the inherent information of multispectral orthoimages in order to train models that will be capable of predicting the pavement condition of different road segments. A preliminary case study was conducted with data provided by the City of Dallas and remote sensing images acquired from the Texas Natural Resources Information System. An image segmentation algorithm was employed to separate pavements from other surfaces, and three different classifiers were compared for pavement condition class prediction. The results showed that up to 67 percent of the sampled segments were correctly classified, indicating that the framework might have potential for future implementation if further research is conducted on the different constituent steps in order to increase classification accuracy.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ50 Standing Committee on Information Systems and Technology.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Politis, Stefanos S
  • Zhang, Zhanmin
  • Kouchaki, Sareh
  • Caldas, Carlos
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: 5p

Subject/Index Terms

• TRT Terms: Case studies; Condition surveys; Data mining; Pavements; Remote sensing
• Uncontrolled Terms: Multispectral orthoimages; Network level management; Pavement conditions
• Geographic Terms: Dallas (Texas)
• Subject Areas: Data and Information Technology; Highways; Pavements;

Filing Info

• Accession Number: 01698428
• Record Type: Publication
• Report/Paper Numbers: 19-01466
• Files: TRIS, TRB, ATRI
• Created Date: Mar 2 2019 3:41PM