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Pavement distress assessment with high-resolution orthoimagery

Presenter(s):  Ben Hickson

Abstract: 

Roadway health impacts many aspects of transportation planning including accessibility, efficiency, maintenance, and funding. However, due to the difficulties and expenses of navigating roadways with equipment and persons to conduct assessments, most corridors are infrequently assessed. The Pima Association of Governments is leveraging high-resolution orthoimagery assets to quantify roadway surface health against the PASER ((Pavement Surface Evaluation and Rating) scale. The imagery coupled with existing roadway distress data is fed through image filters and machine learning algorthims to provide a single metric for surface health on all roadway segments in the region. While the PASER rating system provides only a general metric of pavement health (1-10 scale), it can be used in further screening procedures by planners to advise and coordinate maintenance activities.

Key Takeaway: Primarily tools and methodology employed will be discussed though I will address the needs for this data and its usefulness. Some python programming will be discussed, although mostly in the context of the open-source resources leveraged

Intended Audience: Analysts interested in generating valued derivatives from imagery and planners curious about how imagery can be leveraged to assist in planning activities

About the presenter(s):

Ben's generally interested in all geospatial data science tools for evaluating imagery and LiDAR data and for producing maps and apps. Having previously worked for the University of Arizona Libraries as a systems administrator and research support personnel, he now works with for Pima Association of Governments as a Senior Analyst providing data and map support for regional activities addressed by PAG