Deep Learning for Pavement Condition Evaluation Using Satellite Imagery

TL;DR

This paper explores using deep learning models to analyze satellite imagery for pavement condition assessment, offering a rapid, cost-effective alternative to traditional manual surveys with over 90% accuracy.

Contribution

It introduces a novel application of deep learning to satellite images for pavement evaluation, leveraging recent advancements in satellite imaging and image processing.

Findings

Achieved over 90% accuracy in pavement condition classification.

Utilized a dataset of 3,000 satellite images linked with pavement ratings.

Demonstrated the potential for scalable, automated infrastructure monitoring.

Abstract

Civil infrastructure systems covers large land areas and needs frequent inspections to maintain their public service capabilities. The conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore more cost-effective methods for monitoring and maintaining these infrastructures. Fortunately, recent advancements in satellite systems and image processing algorithms have opened up new possibilities. Numerous satellite systems have been employed to monitor infrastructure conditions and identify damages. Due to the improvement in ground sample distance (GSD), the level of detail that can be captured has significantly increased. Taking advantage of these technology advancement, this research investigated to evaluate pavement conditions using deep learning models for analyzing satellite images. We gathered over 3,000 satellite images of pavement sections, together with pavement evaluation ratings from TxDOT's PMIS database. The results of our study show an accuracy rate is exceeding 90%. This research paves the way for a rapid and cost-effective approach to evaluating the pavement network in the future.