Automated Pavement Assessment

Development of a Pavement Evaluation Tool Using Google Earth Imagery and Deep Learning

Data Acquisition

Using Google Earth imagery for visual condition surveying of highway pavement in the United States. 

A screenshot tool[Link] is developed to automatically track the highway for collecting end-to-end images and GPS[Link].

Data Processing

A highway segmentation tool based on a deep convolutional neural network (DCNN) is developed to segment the collected highway images into the predefined object categories, where the cracks are identified and labeled in each small-patch of the overlapping assembled label-image prediction. 

Data Visualization

The longitudinal cracks and transverse cracks are detected using the x-gradient and y-gradient from the Sobel operator, and the developed pavement evaluation tool rates the longitudinal cracking in 0.3048m/30.48m-Station (linear feet per 100 ft. station) and transverse cracking in number per 30.48m-Station (100 ft. station), which can be visualized in ArcGIS Online.

Demo

Experiments were conducted on Interstate 43 (I-43)[Link] in Milwaukee County with pavement in both defective and sound visual conditions. 

Experimental results showed the patch-wise highway segmentation in Google Earth imagery from the 16×16-pixel DCNN model has as precise pixel accuracy as the U-net based pixelwise crack/non-crack classifier. 

Discussion

Compared to the manually crafted label-image in the experimental area, the rated longitudinal cracking has an average error of overrating 20%, while transverse cracking has an average error of underrating 7%. 

This research project contributes to visual pavement condition surveying methodology with the free-to-access Google Earth imagery, which is a feasible, cost-effective option for accurately rating and geographically visualizing both project level and network level pavement.

References

1. Jiang, Y., Han, S., and Bai, Y. (2021). “Development of a Pavement Evaluation Tool Using Aerial Imagery and Deep Learning.” ASCE Journal of Transportation Engineering, Part B: Pavements, 147(3), DOI: 10.1061/JPEODX.0000282.