Deep Learning for Pavement Condition Evaluation Using Satellite Imagery

2025-08-02

AI Summary

Traditional pavement condition assessment relies heavily on manual inspections or vehicle-mounted sensors, resulting in high operational costs and limited spatial coverage. To address these limitations, this study pioneers the integration of deep learning with high-resolution satellite remote sensing imagery to develop an end-to-end pavement condition assessment model. The model is trained and validated jointly on over 3,000 expertly annotated satellite images and pavement performance data from the Texas Department of Transportation’s Pavement Management Information System (TxDOT PMIS). It achieves over 90% accuracy in both fine-grained distress classification and holistic pavement condition rating. By leveraging satellite imagery, the approach overcomes the spatial constraints of ground-based surveys, enabling large-scale, cost-effective, and efficient intelligent infrastructure monitoring. This work establishes a reproducible technical paradigm and provides empirical validation for satellite remote sensing–enabled health assessment of transportation infrastructure.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Automate pavement condition evaluation using satellite imagery
• Reduce labor-intensive manual infrastructure inspection methods
• Improve cost-effectiveness of large-scale pavement monitoring

Innovation

• Deep learning analyzes satellite pavement images
• High-resolution satellite imagery improves detail capture
• Achieves over 90% accuracy in evaluations