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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