A landslide area segmentation method based on an improved UNet

As remote sensing technology matures, landslide target segmentation has become increasingly important in disaster prevention, control, and urban construction, playing a crucial role in disaster loss assessment and post-disaster rescue. Therefore, this paper proposes an improved UNet-based landslide segmentation algorithm. Firstly, the feature extraction structure of the model was redesigned by integrating dilated convolution and EMA attention mechanism to enhance the model's ability to extract image features. Additionally, this study introduces the Pag module to replace the original skip connection method, thereby enhancing information fusion between feature maps, reducing pixel information loss, and further improving the model's overall performance. Experimental results show that compared to the original model, our model improves mIoU, Precision, Recall, and F1-score by approximately 2.4%, 2.4%, 3.2%, and 2.8%, respectively. This study not only provides an effective method for landslide segmentation tasks but also offers new perspectives for further research in related fields.