Landslides pose a significant threat to human life and infrastructure which urges the need for efficient techniques for identifying and categorising them. The advent of deep segmentation models such as the Segformer has shown a remarkable empirical performance for semantic segmentation tasks on well-known benchmark datasets, such as ADE20k and Cityscapes. Therefore, this research proposes utilising Segformer on the benchmark Chinese Academy of Sciences (CAS) Landslide Dataset, which features high-quality aerial images of areas impacted or prone to landslides. Taking advantage of the multi-scale attention mechanism and long-range dependency modeling characteristics of the Segformer architecture, this research aims to achieve state-of-the-art results for landslide segmentation using aerial images. Experimental results show the advantage of the Segformer model in segmenting landslide areas, with the largest Segformer variant achieving an Intersection over Union (IoU) score of 87.795% on the Unmanned aerial vehicle (UAV) dataset, surpassing the previous state-of-the-art model, Multiscale Feature Fusion and Enhancement Network (MFFENet), by 3.4%. On the Satellite (SAT) dataset, Segformer attained an IoU score of 79.300%, outperforming the previous best model, DeepLabv3+, by 11.163%. For the combined UAV&SAT dataset, Segformer achieved an IoU score of 85.157%, surpassing DeepLabv3+, the best previous model by 5.032%.