Knowledge about historic landslide event occurrences is important for supporting disaster risk reduction strategies. Building upon findings from the 2022 Landslide4Sense competition, we propose a workflow based on a deep neural network architecture for landslide detection and segmentation from multi-source remote sensing image input. We use a U-Net trained with cross entropy loss as baseline model. We then improve this model by leveraging a wide range of deep learning techniques. In particular, we conduct feature engineering by generating new band data from the original bands, which helps to enhance the quality of the remote sensing image input. Regarding the network architecture, we replace traditional convolutional layers in the U-Net baseline by a residual-convolutional layer. We also propose an attention layer, which leverages the multi-head attention scheme. Additionally, we generate multiple output masks with three different resolutions, which creates an ensemble of three outputs in the inference process to enhance the performance. Finally, we propose a combined loss function, which leverages on focal loss and IoU loss to train the network. Our experiments on the development set of the Landslide4Sense challenge achieve an F1 score and an mIoU score of 84.07 and 76.07, respectively. Our best model setup outperforms the challenge baseline and the proposed U-Net baseline, improving the F1 score and the mIoU score by 6.8/7.4 and 10.5/8.8, respectively.