Automated Extraction of Weather Variables from Imagery

Details

• Creators:
  Wiener, Gerry ; Petzke, William ; Brummet, Tom ; Walker, Curtis
• Corporate Creators:
  National Center for Atmospheric Research (U.S.) ; Iowa State University. Aurora Program
• Corporate Contributors:
  Iowa Department of Transportation. Aurora Program ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Data fusion
  • Decision support systems
  • Image processing
  • Maintenance management
  • Neural networks
  • Road weather information systems
  • Winter maintenance
• Publication/ Report Number:
  Aurora Project 2021-06
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Iowa Department of Transportation
• Abstract:
  Department of transportation (DOT) maintenance supervisors utilize a variety of tools including maintenance decision support systems (MDSS) to gain a better understanding of current and future road surface conditions (RSC) during winter weather. MDSS automatically attempt to deduce current RSC based on road weather information system (RWIS) and other data to a greater or lesser degree of accuracy. Although current MDSS implementations present highway camera imagery, they typically do not incorporate automated camera image recognition in order to improve the MDSS assessment of winter RSC. Thus, there can be discrepancies between the road weather conditions in camera images and MDSS RSC assessments.
• Format:
  PDF
• Funding:
  TPF-5(435) and Federal SPR Part II, CFDA 20.205
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73088/dot_73088_DS1.pdf
• File Type:
  [PDF - 2.15 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:24e07d19bfacb6d9a09726a5205abbbd0090a7f419b29255f52a32ecf80bf26444fac57d29a2a1fec5c18ba92fc110b978263a6dfbaa4dd3be69359a11748117