The purpose of this study is to explore the applicability of satellite-based synthetic aperture radar (SAR) data combined with pavement management system (PMS) indicators for effective road condition monitoring on mountainous local roads. Field survey data, including the International Roughness Index (IRI) and rutting measurements, were used as the ground truth, whereas Sentinel-1 and COSMO-SkyMed SAR images were processed using the time-series InSAR analysis to detect surface displacement and pavement deformations. In addition, a deep learning framework integrating PMS data and SAR imagery was developed, consisting of a swine transformer and CNN–LSTM networks for the classification and localization of pavement defects. The results demonstrated that X-band SAR backscatter values were correlated with IRI variations and that the proposed hybrid two-stage approach (CNN for surface damage and LSTM for rutting) enhanced the accuracy of defect detection compared with conventional single-model approaches. These findings highlight the potential of combining remote sensing and AI-based analysis with existing PMS datasets to provide a cost-effective and scalable solution for road asset management and maintenance prioritization.

ABSTRACT
1. 서론
2. 연구방법론
    2.1. 데이터 수집
    2.2. 분석 기법
3. 결과 분석
    3.1. PMS 데이터 분석
    3.2. 위성 SAR 기반 도로 상태 분석
4. 결론
감사의 글
REFERENCES

• 김연태(한국건설기술연구원 도로교통연구본부 전임연구원) | Kim Yeon Tae Corresponding author