  Knowledge about historic landslide event occurrence is important for
supporting disaster risk reduction strategies. Building upon findings from 2022
Landslide4Sense Competition, we propose a deep neural network based system for
landslide detection and segmentation from multisource remote sensing image
input. We use a U-Net trained with Cross Entropy loss as baseline model. We
then improve the U-Net baseline 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 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 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/mIoU score by 6.8/7.4
and 10.5/8.8, respectively.
