Fully Automated Unsupervised Machine Learning Framework for Mapping Erosion Hotspots in Quick Clay Areas Using Remote Sensing–Derived Data

Quick clays are fine-grained, highly sensitive marine deposits that are widespread across formerly glaciated regions, including Norway, Sweden, Finland, and Canada. The low remoulded strength of the quick clays makes them particularly susceptible to extensive retrogressive landslides, which pose serious challenges to society. Erosion is recognized as one of the most important pre-conditioning and triggering factor for quick clay landslide. Therefore, identification of the erosion hotspots is essential for understanding landslide initiation processes and for effective hazard mitigation in quick clay terrains. Machine learning has emerged as an effective tool for erosion hotspot mapping, allowing complex spatial patterns and nonlinear interactions among erosion-controlling factors to be identified from remote sensing–derived data. Recent studies have demonstrated that Deep Neural Networks can be effectively employed to identify erosion-prone zones in quick clay environments when sufficient labelled data are available. This study investigates whether unsupervised machine learning applied to remote sensing–derived data can effectively identify erosion hotspots in quick clay areas. A fully automated, Python-based workflow was developed for erosion hotspot mapping in quick clay areas using remote sensing–derived data. The dataset includes terrain, hydrological, environmental, and anthropogenic parameters relevant to erosion and slope instability. Initially, a total of twenty input parameters were considered. Pearson correlation coefficients were computed to assess inter-feature dependencies, and principal component analysis (PCA) was employed to evaluate feature importance. The unsupervised analysis was performed using multiple clustering techniques to capture different structural characteristics of the data where each cluster represents a distinct level of erosion susceptibility. The results suggest that the proposed unsupervised framework can effectively delineate erosion hotspots in quick clay areas and constitutes an initial step toward the development of early warning systems.
Acknowledgements
This work was supported by the Research Council of Norway through the SAFERCLAY project (Grant No. 352887). Orkun Türe was supported by the Council of Higher Education of Türkiye under the DOSAP scholarship programme and served as a visiting researcher at NGI and NTNU.