In recent years, remote sensing technology has been extensively used in detecting and managing natural disasters, playing a vital role in the early identification of events like landslides. The integration of deep learning models has considerably enhanced the efficiency and accuracy of landslide detection particularly in automating the analysis and quickly identifying affected areas. However, existing models often face challenges, such as incomplete feature extraction, loss of contextual information, and high computational complexity. To overcome these challenges, we propose an innovative landslide detection model, Lights-Transformer, which is designed to improve both the accuracy and efficiency. This model employs an encoder–decoder architecture that incorporates multi-scale contextual information and an efficient attention mechanism, effectively capturing both local and global features of images while minimizing information loss. By introducing a Fusion Block for enhanced multi-angle feature fusion and a Light Segmentation Head to boost inference speed, Lights-Transformer extracts detailed feature maps from high-resolution remote sensing images, enabling the accurate identification of landslide regions and significantly improving detection accuracy. Compared to existing state-of-the-art landslide detection models, Lights-Transformer offers considerable advantages in accuracy, precision, and computational efficiency. On the GDCLD dataset, Lights-Transformer achieves an mIoU of 85.11%, accuracy of 97.44%, F1 score of 91.49%, kappa value of 82.98%, precision of 91.46%, and recall of 91.52%, demonstrating its exceptional performance.