Global landslide mapping using U-Net architecture with diverse backbones across multi-regional and multi-sensor remote sensing datasets

Landslides remain a constant hazard to societies in hilly regions worldwide, necessitating accurate and scalable detection techniques. In this study, we assess the performance of cutting-edge deep learning architecture, specifically U-Net, combined with seven backbone networks, including EfficientNet, ResNet, Inception, MobileNet, VGG, ResNeXt, and SENet, and their 29 variants, for the semantic segmentation of landslides using multiple high-resolution remote sensing datasets. Our experiments encompass two geographically diverse and challenging datasets, primarily the High-Resolution Global Landslide Detector Database (HRGLDD) (which includes South/Southeast Asia, East Asia, and Latin America), and the Large-scale Multi-source High-resolution Landslide Dataset (LMHLD). Performance is evaluated using standard segmentation metrics, including Intersection over Union (IoU), Precision, Recall, and F-score. The experimental results underscore the superior performance of the Squeeze-and-Excitation (SE) family of backbone networks, notably SENet154, SE-ResNet-152, and SE-ResNeXt-50/101, across all three decoder architectures. Specifically, the U-Net + SE-ResNeXt_101 model achieved the highest F-score of 0.9569, followed by U-Net + SE-ResNeXt_50 with an F-score of 0.9471 on the HRGLDD dataset. The study provides a comprehensive benchmark of encoder-decoder combinations for landslide mapping, emphasizing the importance of backbone selection in achieving segmentation accuracy. Our findings serve as a valuable resource for future remote sensing applications in geohazard mapping, particularly in regions with limited ground truth availability.