Change detection-based potential landslides in three prone areas of South Korea

Geological, morphological, and meteorological conditions have made South Korea prone to landslides. This study proposes a change detection method to detect landslides in the Yecheon, Yeongju, and Jeongseon areas of South Korea. We compared the performances of support vector machine (SVM), maximum likelihood (ML), and random tree (RT) algorithms for detecting potential landslides. The process begins by classifying PlanetScope images taken before and after reported landslides that occurred during the 2023 rainy season. Our evaluation showed that SVM model outperformed the other two models, achieving mean precision, recall, and F1-score values of 60.92%, 80.15%, and 68.24%, respectively; RT and ML algorithms had accuracy metrics that were lower by 2%–8%. Approximately 369 potential landslides were detected in the Yecheon area, 412 in the Yongju area, and 108 in the Jeongseon area based on the SVM data. The proposed method enables rapid and effective generation of a potential landslide map, offering valuable insights for the development of mitigation measures and prevention policies.