A proposed method for landslide detection based on transfer learning and graph neural network

Abstract

Rapid landslide detection can give timely information for emergency responses when group-occurring landslides occurred. However, it is frequently difficult to quickly acquire sufficient data for landslide detection in a short period. Transfer learning harnesses the knowledge of landslide detection from the source domain to the target domain with little labeled data. Graph neural networks (GNN) explicitly models global or local relationships by constructing a graph structure where nodes represent pixels and edges represent connections, thereby improving segmentation consistency. Here, we proposed a deep learning model integrated the attention mechanism, multiscale connections, and GNN to capture contextual information and extract the important features for landslide detection. The proposed method was first pretrained in the large-scale dataset, then transferred and fine-tuned the parameters in the two case studies: 2013 Niangniangba rainfall-induced landslides in China and 2018 Hokkaido coseismic landslides in Japan. We examined the feasibility of the proposed model and studied how much impact the scale of the target domain would have on the landslide detection. The controlled experiments reported that our proposed method could achieve the best F1-score in the data-rich condition. Our results also reveal that the deep learning models with transfer learning in data-limited conditions can perform closely to those in data-rich conditions. The fine-tuning model updated parameters in the target domain besides gaining knowledge from the source domain; hence, performance was improved significantly in a new region despite having little new data. Our approach demonstrates a potential way to improve landslide detection assessment, particularly in areas where landslides are extremely difficult to label.

Keywords

Deep learning / Transfer learning / Graph convolution network / Data deficiency / Group-occurring landslides

CRediT authorship contribution statement

Wanqi Luo: Writing - original draft, Visualization, Methodology. Haijun Qiu: Writing - review & editing, Supervision, Project administration, Funding acquisition, Conceptualization. Yingdong Wei: Formal analysis. Wenchao Huangfu: Resources, Data curation. Dongdong Yang: Validation, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was funded by the National Natural Science Foundation of China (Grant No. 42471083, 42271078), Key Research and Development Program of Shaanxi (2024SF-YBXM-669), and China Postdoctoral Science Foundation (Grant No. 2022M722564).

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