“Fusion network with attention for landslide detection. Application to Bijie landslide open dataset”

Remote sensing techniques are now widely spread for the early detection of ground deformation, implementation of warning systems in case of imminent landslide triggering, and medium- and long-term slope instability monitoring. The large breadth of data available to the scientific community, associated with processing techniques improved as the data volume was increasing, has led to noticeable developments in the field of remote sensing data processing, using machine learning algorithms and more particularly deep neural networks.

 

This arsenal of data and techniques is necessary for the present scientific challenges the community of researchers on landslides still have to meet. As landslides can be complex, for risk management and disaster mitigation strategies, it is necessary to have a precise idea of their location, shape, and size to be studied and monitored. The challenge aims to automate landslide detection and mapping, especially through learning methods. Machine learning methods based on Deep Neural Networks have recently been employed for landslide studies and provide promising efficient results for landslide detection [1].

 

In this study, we propose an original neural network for landslide detection. More precisely, we exploit a fusion network [1] dealing with optical images on the one hand and Digital Elevation Models on the other hand. To improve the results, attention layers [3] (able to stabilize the training and more precise results) as well as mix up techniques [4] (able to generalize more efficiently) are exploited.

The model was trained and tested on the open Bijie landslide dataset.

 

Keywords: Remote sensing for landslide monitoring and detection, landslide detection, deep neural networks, attention

 

[1] Ji, S., Yu, D., Shen, C., Li, W., & Xu, Q. (2020). Landslide detection from an open satellite imagery and digital elevation model dataset using attention-boosted convolutional neural networks. Landslides, 17(6), 1337-1352.

[2] Song, W., Li, S., Fang, L., & Lu, T. (2018). Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing, 56(6), 3173-3184.

[3] Niu, Z., Zhong, G., & Yu, H. (2021). A review on the attention mechanism of deep learning. Neurocomputing, 452, 48-62.

[4] Thulasidasan, S., Chennupati, G., Bilmes, J. A., Bhattacharya, T., & Michalak, S. (2019). On mixup training: Improved calibration and predictive uncertainty for deep neural networks. Advances in Neural Information Processing Systems, 32.