The remote sensing method for large-scale asphalt pavement aging assessment with automated sample generation and deep learning
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new framework for assessing asphalt pavement aging using remote sensing and deep learning. It enables fast, accurate large-area evaluations for improved road maintenance and traffic safety.
Area Of Science
- Remote Sensing
- Deep Learning
- Pavement Engineering
Background
- Asphalt pavement aging assessment is crucial for road maintenance and traffic safety.
- Traditional manual surveys are inefficient and time-consuming for large-scale monitoring.
Purpose Of The Study
- To develop an innovative framework for rapid and accurate assessment of asphalt pavement aging over large areas.
- To integrate multi-endmember mixed pixel unmixing, automated sample generation, and deep learning for pavement aging evaluation.
Main Methods
- Utilized WorldView-3 remote sensing data for sample generation.
- Employed multi-endmember spectral unmixing and neighborhood filtering to create high-quality training and validation samples.
- Applied a one-dimensional convolutional neural network (1D-CNN) with an unsupervised zero-shot transfer approach for model training and inference.
Main Results
- Achieved high classification accuracy (95.95%) and Kappa coefficients (0.9459) in study area-I.
- Demonstrated good performance in study area-II with 89.70% accuracy and 0.8628 Kappa coefficient.
- Validated the effectiveness of the proposed framework for large-area asphalt pavement aging assessment.
Conclusions
- The integrated framework provides an effective solution for rapid asphalt pavement aging assessment.
- Results support improved pavement maintenance decision-making and traffic safety warning systems.
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