Automated Landslide Inventory Mapping Using SAMLoRA and Hillshade Datasets: A Deep Learning Approach

Landslide inventories are essential for hazard assessment and risk mitigation, yet their accurate and efficient creation remains a challenge, particularly in forested and topographically complex regions. Traditional approaches relying on RGB imagery often struggle with dense vegetation cover, which obscures landslide features. In this study, we propose an innovative deep learning framework utilizing the Segment Anything Model with Low-Rank Adaptation (SAMLoRA) to automatically detect and map landslides from hillshade datasets. Hillshade representations, derived from high-resolution Digital Elevation Models (DEMs), provide enhanced visibility of topographic features by emphasizing surface morphology independent of vegetation cover.

Our model was trained on a diverse dataset collected from Romania, Czechia, and Italy, comprising over 5,000 manually delineated landslide polygons. By leveraging the SAMLoRA model, which combines the robust segmentation capabilities of SAM with the adaptability of LoRA for domain-specific fine-tuning, we achieve superior landslide detection performance compared to RGB-based methods. Our approach effectively identifies landslides even in densely forested areas, where traditional image-based techniques often fail. Experimental results demonstrate that the SAMLoRA model achieves an accuracy exceeding 80%, significantly improving both precision and recall while reducing manual mapping efforts.

This study highlights the potential of deep learning applied to topographic derivatives, paving the way for more reliable and automated landslide inventory mapping in diverse and challenging environments.