A remote sensing and GIS-enabled asset management system (RS-GAMS).
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A remote sensing and GIS-enabled asset management system (RS-GAMS).

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      Under U.S. Department of Transportation (DOT) Commercial Remote Sensing and Spatial Information (CRS&SI) Technology Initiative 2 of the Transportation Infrastructure Construction and Condition Assessment, an intelligent Remote Sensing and GIS-based Asset Management System (RS-GAMS) was developed and validated in this research project by integrating CRS&SI technology that can be operated non-destructively at highway speed to improve roadway asset management including pavements and traffic signs. For pavement asset, the validation focused on the automatic detection and measurement of asphalt pavement cracking and rutting using the emerging 3D line laser imaging technology (abbreviated as “3D line laser” thereafter), which operates at highway speed and captures the full-lane-width range (depth) change of pavement surface. As far as automatic pavement crack detection is concerned, this new technology has the inherent advantage in comparison with the traditional line scan cameras that suffer from ambient lighting conditions and pavement surface stains. In addition, the high-resolution and high-accuracy range data can be conveniently utilized to measure network-level asphalt pavement rutting and detect isolated ruts. The successful validation would provide transportation agencies an “all-in-one” technology for pavement condition assessment with higher accuracy and extended capabilities. Traffic signs are critical utilities for roadway safety and traffic regulation. The latest Manual on Uniform Traffic Control Devices (MUTCD) required each transportation agency to maintain the signs with an acceptable level of retroreflectivity. Thus, for traffic asset, the validation focused on the efficient sign inventory data collection and sign retroreflectivity condition assessment. Due to the fact that a state transportation agency needs to maintain millions of signs on roadways, it is very time-consuming and costly for sign inventory data collection by means of the paper-pencil method, handheld-based method, or even the method of reviewing millions of roadway video log images. This research project validated an enhanced sign inventory procedure by integrating various sensing technologies such as video log images, mobile Light Detection and Ranging (LiDAR) data, and image processing algorithms. In addition, mobile LiDAR was also evaluated for detecting sign retroreflectivity conditions because the traditional methods are either labor intensive or very inaccurate.
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