Pavement Condition Assessment Using Multisource Satellite Data and Deep Learning

Abstract

Effective monitoring of pavement conditions plays an important role in ensuring road safety and optimizing maintenance. Traditional inspection methods are labor-intensive and limited in coverage, motivating the use of remote sensing and deep learning for scalable alternatives. This study proposes a novel framework for classifying pavement type and condition by integrating medium-resolution optical imagery from Planet satellite and synthetic aperture radar (SAR) data from Sentinel-1. A deep learning dataset was constructed by combining eight bands and labeling them using IRI data collected over multiple years. Two semantic segmentation models, U-Net and DeepLabV3, were trained and evaluated. Also, a series of band-drop scenarios was tested to assess the importance of each spectral and radar band. Results showed that DeepLabV3 outperformed U-Net in both overall accuracy and weighted F 1 -score. Particularly, DeepLabV3 was more successful in identifying fair and poor asphalt pavements, which are critical for proactive maintenance decisions. The analysis illustrates that combining SAR with optical data consistently improved results compared to using either source independently. These findings demonstrate the feasibility and effectiveness of using multisource satellite imagery with deep learning for scalable, cost-efficient pavement condition assessment. The proposed framework offers transportation agencies a practical solution for enhancing infrastructure monitoring and data-driven decision-making.