State-of-the-art remote sensing geospatial technologies in support of transportation monitoring and management.

摘要

The widespread use of digital technologies, combined with rapid sensor advancements resulted in a paradigm shift in geospatial technologies the end of the last millennium. The improved performance provided by the state-of-the-art airborne remote sensing technology created opportunities for new applications that require high spatial and temporal resolution data. Transportation activities represent a major segment of the economy in industrialized nations. As such both the transportation infrastructure and traffic must be carefully monitored and planned. Engineering scale topographic mapping has been a long-time geospatial data user, but the high resolution geospatial data could also be considered for vehicle extraction and velocity estimation to support traffic flow analysis.;The objective of this dissertation is to provide an assessment on what state-of-the-art remote sensing technologies can offer in both areas: first, to further improve the accuracy and reliability of topographic, in particular, roadway corridor mapping systems, and second, to assess the feasibility of extracting primary data to support traffic flow computation. The discussion is concerned with airborne LiDAR (Light Detection And Ranging) and digital camera systems, supported by direct georeferencing. The review of the state-of-the-art remote sensing technologies is dedicated to address the special requirements of the two transportation applications of airborne remotely sensed data. The performance characteristics of the geospatial sensors and the overall error budget are discussed. The error analysis part is focused on the overall achievable point positioning accuracy performance of directly georeferenced remote sensing systems.;The QA/QC (Quality Assurance/Quality Control) process is a challenge for any airborne direct georeferencing-based remote sensing system. A new method to support QA/QC is introduced that uses the road pavement markings to improve both sensor data accuracy as well as the position of road features. The identification of the pavement markings is based on LiDAR intensity data and is guided by the ground control information available. The centerline of the markings in LiDAR data is modeled and matched to the reference data, providing the observation to the QA/QC process.;The discussion on the innovative use of remote sensing technologies investigates the feasibility of providing remotely sensed traffic data for monitoring and management. An advantage of air-based platforms, including manned and unmanned fixed-wing aircraft and helicopters, is that they can be rapidly deployed to observe traffic incidents that occur in areas where there are no ground-based sensors. To support vehicle extraction from airborne imagery, a method was introduced that provides a true object scale data representation that can facilitate the vehicle extraction. The vehicle extraction from LiDAR data was followed by coarse classification of the extracted vehicles to support coarse velocity estimation; basically, grouping the vehicles into three major categories based on their size. Finally, a novel method was introduced for simultaneously acquired LiDAR and image data, which can combine the advantages of the two sensors for obtaining better velocity estimates of LiDAR-extracted vehicles.