Efficient Detection of Earthquake−Triggered Landslides Based on U−Net++: An Example of the 2018 Hokkaido Eastern Iburi (Japan) Mw = 6.6 Earthquake

Abstract

Efficient detection of earthquake−triggered landslides is crucial for emergency response and risk assessment. With the development of multi−source remote sensing images, artificial intelligence has gradually become a powerful landslide detection method for similar tasks, aiming to mitigate time−consuming problems and meet emergency requirements. In this study, a relatively new deep learning (DL) network, called U−Net++, was designed to detect landslides for regions affected by the Iburi, Japan Mw = 6.6 earthquake, with only small training samples. For feature extraction, ResNet50 was selected as the feature extraction layer, and transfer learning was adopted to introduce the pre−trained weights for accelerating the model convergence. To prove the feasibility and validity of the proposed model, the random forest algorithm (RF) was selected as the benchmark, and the F1−score, Kappa coefficient, and IoU (Intersection of Union) were chosen to quantitatively evaluate the model’s performance. In addition, the proposed model was trained with different sample sizes (256,512) and network depths (3,4,5), respectively, to analyze their impacts on performance. The results showed that both models detected the majority of landslides, while the proposed model obtained the highest metric value (F1−score = 0.7580, Kappa = 0.7441, and IoU = 0.6104) and was capable of resisting the noise. In addition, the proposed model trained with sample size 256 possessed optimal performance, proving that the size is a non−negligible parameter in U−Net++, and it was found that the U−Net++ trained with shallower layer 3 yielded better results than that with the standard layer 5. Finally, the outstanding performance of the proposed model on a public landslide dataset demonstrated the generalization of U−Net++.

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

“…At present, landslide identification methods based on remote sensing are broadly classified into (1) visual interpretation, (2) change detection, (3) ML, and (4) DL. The features of the data and processing parameters are not required to be set artificially except for the necessary parameters in DL, which greatly enhances the portability of landslide detection [143]. Yu et al [144] proposed a hierarchical attention deconvolution neural network, HADeenNet, which is specifically designed to detect landslides from high spatial resolution images.…”

Automatic Detection of Forested Landslides: A Case Study in Jiuzhaigou County, China

“…Attention-based deep neural networks were used by Amankwah et al [18] to identify landslides from bitemporal satellite imagery. Yang and Xu [19] identified earthquake-induced landslides in Hokkaido using U-Net++. Existing models for automatic landslide detection rely heavily on optical remote sensing images.…”