Remote sensing of multimodal transportation systems.

Details

• Creators:
  Bridgelall, Raj ; Rafert, James B. ; Tolliver, Denver
• Corporate Creators:
  Upper Great Plains Transportation Institute ; North Dakota State University
• Corporate Contributors:
  Mountain-Plains Consortium
• Subject/TRT Terms:
  Multimodal Transportation Remote Sensing
• Publication/ Report Number:
  MPC 16-313
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  Hyperspectral remote sensing is an emerging field with many potential applications in the observation, management, and maintenance of the global transportation infrastructure. This report describes the development of an affordable framework to capture hyperspectral images and models to classify the images. The framework and models enable new approaches to plan, analyze, and assess the performance of multimodal transportation systems. Every hyperspectral image frame contains information in wavelengths that extend well beyond those that humans are capable of seeing or perceiving. The rapid size and cost reduction of both unmanned aircraft systems and hyperspectral image sensors enable easy scaling of the framework. Scaling is achieved simply by conducting multiple parallel missions to achieve broad area coverage at affordable prices. The authors showcase the general utility of the framework to
  
  enhance models used for roadway congestion forecasting, railway condition monitoring, and pipeline risk management. The authors offer additional insights by demonstrating a specific utility of the framework and models for the rapid detection of hazardous spills. Practitioners who utilize the framework and models to implement hyperspectral remote sensing platforms will benefit from greater situational awareness to make informed decisions in transportation systems development, operations, and maintenance.
  
  
• Format:
  PDF
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:254ad6536a944d11c9eb47c113fce764f157d45966bb957842b6f5b45a7eaf25a8cfdadeb1a8def0e17812e8a4c8c58e8a5e1f7eeb9a66d948b8a2dae8dc6ede
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31486/dot_31486_DS1.pdf
• File Type:
  [PDF - 2.17 MB ]