Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides.

Learning long-range contextual dependence is important for remote sensing image segmentation in complex patterns. Meanwhile, exploring local context information is conducive to the discrimination of fine details. Only underlining either global semantic correlations or local context details is insufficient to achieve accurate segmentation. In this paper, we propose an architecture with the global-local self-attention (GLSA) mechanism, called GLSANet, which can simultaneously consider both global and local contexts for segmentations. Particularly, the GLSA mechanism consists of the global atrous self-attention (GASA) and local window self-attention (LWSA) mechanisms. GASA can learn long-range semantic relations in a gapped manner, while LWSA can locally capture contextual details. As a bridge between the two self-attention branches, a context fusion module is further designed to adaptively integrate global and local contexts. The experiments with public datasets show that the proposed GLSANet significantly refines the semantic segmentation and outperforms other competing methods.