Post-Earthquake Landslide Extraction Based on Feature Expansion U-Net Model.

Earthquake-induced landslide is the most common geological disaster caused by earthquakes, which seriously threatens the safety of human life and property. Rapid access to landslide information after the earthquake is the key to disaster mitigation and relief. The existing landslide extraction methods all require a lot of manual intervention and cannot provide timely and effective earthquake rescue information. In order to solve the above problems, this paper improves the traditional U -Net algorithm by feature expansion and proposes a FE-U-Net. This new method is applied to the landslide in Jiuzhaigou County, Sichuan Province, China by combining multi-source remote sensing images. The experimental results show that the F1-score, Kappa coefficient and mIOU values obtained by the proposed FE-U-Net are 95.02%, 94.23% and 94.47%, respectively, which are all about 3% higher than the traditional U -Net. In addition, the model training time is less than 10s longer than the traditional U -Net model.