Using Remote Sensing to Measure Pavement Surface Condition and Distress: A New Brunswick Pilot Test

Pavement surface distresses can be indicators of underlying structural issues on roads but require over-the-road or field surveys which can limit monitoring on extensive road networks to multiyear cycles. Advances in remote sensing imaging and image processing offer considerable potential to complement existing road-based surface distress measurement. If pavement surface distress observed from aerial or satellite imagery can be quantified, and the assessment is comparable to that obtained from field surveys, it may be possible to automate provincial or national-level network assessments from this imagery. This paper presents the results of a preliminary assessment of various types of aerial and satellite imagery for their potential to be used to identify road surface distresses. Qualitative assessments in terms of potential applicability for remote sensing (e.g. high, low, and not applicable) were conducted by exploring road surface distresses according to ASTM D6433-18 and included three different road functional classes in New Brunswick. Field survey data for pavement surface condition was obtained from the New Brunswick Department of Transportation and Infrastructure (NBDTI) for two National Highway System routes. The images were assessed according to ASTM-D6433-18 and compared to NBDTI condition data, and a pilot test was conducted on enhancing the highest-resolution imagery available. Select distresses were only observable at high severity in the highest-resolution imagery (SNB Orthoimagery at 0.10 m resolution); no distresses could be distinguished from any of the satellite imagery. Additional image processing was conducted on existing SNB Orthoimages at 0.10 m resolution; cracks that could be barely seen in the original aerial orthoimage can be detected by the Laplacian edge detector. While there have been advancements in this field since the time this work was originally completed in 2019, research continues to focus on segment-by-segment evaluation. There continues to be a need for network-wide applications of results. Artificial Intelligence (AI) can play an important role in the process.