EP-UNet++: Advancing Landslide Monitoring Through Enhanced Remote Sensing Semantic Segmentation

Landslide deformation monitoring is crucial for mitigating geological hazards and ensuring public safety in disaster-prone regions. Remote sensing imagery offers unparalleled advantages in capturing large-scale, high-resolution terrain data for landslide monitoring, but its effectiveness is hindered by challenges in accurately segmenting complex terrain features and delineating boundaries. This study proposes EP-UNet++, a hybrid architecture that integrates UNet++, Pyramid Vision Transformer (PVT), and EfficientNet through multi-level cross-model fusion, cross-resolution alignment, and 11-channel input adaptation. The framework effectively combines local and global features, substantially enhancing the accuracy and generalization of landslide remote sensing image segmentation. Additionally, Local Interpretable Model-agnostic Explanations (LIME) analysis is employed to interpret the contribution of each input factor to the model’s predictions. Experimental results show that the EP-UNet++ achieves a mean Intersection over Union (mIoU) of 74.15, a mean Dice coefficient (mDice) of 85.16, and specificity of 0.979. It achieves an improvement of roughly 4% in mIoU and 2% in mDice over the second-best model, Swin-UNet. The model also exhibits faster convergence during training and robust generalization for landslide pixel detection. These results highlight the EP-UNet++ as a highly effective approach for precise and reliable landslide remote sensing image segmentation, with potential benefits for cost-effective environmental monitoring and disaster mitigation.