MED-DeepLabv3+: a lightweight landslide recognition algorithm on multi-scale remote sensing images

Abstract Rapid landslide identification through remote sensing imagery plays a critical role in post-disaster emergency management. While deep learning approaches have shown promise for semantic segmentation, existing models face limitations in extraction accuracy and processing efficiency for landslide detection applications. We propose the MED-DeepLabv3+ model to resolve these challenges. The proposed model incorporates three key improvements: (1) Replacing Xception in the encoder with the MobileNetV3 feature extraction network enhances the model’s focus on landslide semantic features and reduces the size of the model parameters; (2) Integrating an Efficient Multi-scale Attention module to refine shallow feature representations and enhance multi-scale feature extraction; (3) Introducing a DS-ASPP module, which replaces standard convolutions with Depthwise Separable Convolutions and incorporates Squeeze-and-Excitation and Strip Pooling modules to improve deep feature recognition. Additionally, the singularity of publicly available landslide datasets poses a significant challenge for training deep learning models in this domain. To mitigate this issue, we construct a diverse deep learning-based landslide dataset tailored for landslide recognition research. Results demonstrate that MED-DeepLabv3 + achieves superior performance in landslide detection, obtaining a Mean Intersection over Union (MIoU) of 81.54%, pixel accuracy of 94.63%, precision of 77.69%, recall of 86.35% and an F1-score of 81.79%. Compared to the baseline DeepLabv3 + model, MED-DeepLabv3+ achieves higher detection accuracy while maintaining a lightweight architecture, making it well-suited for rapid and precise landslide identification.