Rapid and accurate mapping of earthquake-triggered landslides from satellite imagery is critical for emergency response and hazard assessment, yet remains challenging due to irregular boundaries, extreme size variations, and atmospheric noise. This paper proposes BiFusion-LDSeg, a novel bi-directional fusion enhanced latent diffusion framework that synergistically combines CNN-Transformer architectures with generative diffusion models for robust landslide segmentation. The framework introduces three key innovations: (1) a dual-encoder with Bi-directional Attention Gates (Bi-AG) enabling sophisticated cross-modal feature calibration between local CNN textures and global Transformer context; (2) a conditional latent diffusion process operating in learned low-dimensional landslide shape manifolds, reducing computational complexity by 100× while enabling inference with only 10 sampling steps versus 1000+ in standard diffusion models; and (3) a boundary-aware progressive decoder employing multi-scale reverse attention mechanisms for precise boundary delineation. Comprehensive experiments on three earthquake datasets from Sichuan Province, China (Lushan Mw 7.0, Jiuzhaigou Mw 6.5, Luding Mw 6.8) demonstrate superior performance, outperforming state-of-the-art methods by 7–13% in IoU and 5–7% in DSC across all three datasets. The framework exhibits exceptional noise robustness, strong cross-dataset generalization, and inherent uncertainty quantification, enabling reliable deployment for post-earthquake landslide inventory mapping at regional scales.

Ascertaining the precise and accurate spatial distribution of population is essential in conducting effective urban planning, resource allocation, and emergency rescue planning. The random forest (RF) model is widely used in population spatialization studies. However, the complexity of population distribution characteristics and the limitations of the RF model in processing unbalanced datasets affect population prediction accuracy. To address these issues, a population spatialization model that integrates feature selection with an improved random forest is proposed herein. Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. The random forest was constructed using feature subsets that were selected via different feature selection methods, namely MIC-RF, RFECV-RF and MDA-RF. Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. Based on this model, a spatial population distribution dataset of the Southern Sichuan Economic Zone at a 500m resolution was generated. Finally, the population dataset generated in this study was compared and validated with the WorldPop dataset. The results showed that utilizing feature selection methods improves model accuracy to varying degrees compared with RF based on all factors, and the MDA-RF had the lowest MAPE of 0.174 and the highest R² of 0.913 among them. Therefore, feature factors selection using the MDA method was considered the optimal feature subset. Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. Compared with the WorldPop dataset, the accuracy of the results predicted using the proposed method was enhanced. The MRE and RMSE of the WorldPop dataset were 57.24 and 23174.98, respectively, while the MRE and RMSE of the proposed method were 25.00 and 15776.50, respectively. This implies that the method proposed in this paper could simulate population spatial distribution more accurately.

In recent years, seismic activity in the southern Sichuan region has increased significantly. Frequent moderate-to-strong earthquakes have caused severe building damage, casualties, and substantial economic losses, making regional seismic risk increasingly prominent. Based on historical seismic catalogs, geological settings, macroseismic intensity data, and strong motion records, this study systematically analyzes regional seismicity, spatial distribution, and strong ground motion characteristics, and quantitatively investigates the distribution and variation of seismic intensity. It further explores the impacts of earthquakes on various building structures, geological disaster chains, lifeline engineering, and human safety, as well as the underlying damage mechanisms. Finally, targeting the widely existing brick masonry structures, this paper proposes cost-effective and easy-to-implement seismic reinforcement measures combined with typical failure modes and casualty causes. The results provide a scientific basis for seismic disaster prevention planning, engineering seismic practice, and risk management in southern Sichuan and comparable regions.

1. As one of the most severe natural disasters (Massazza et al. 2019), earthquakes often cause major casualties and economic losses due to their suddenness and destructive power, posing a serious t...

Earthquake-induced landslide reactivation poses a sustained hazard for years following major seismic events, yet operational prediction remains constrained by heterogeneous multi-modal data, sparse supervision, and the absence of uncertainty-aware frameworks. This paper presents PIMSEL, a physically guided multi-modal semi-supervised framework for post-seismic landslide reactivation risk assessment. PIMSEL integrates satellite-derived morphological features, precipitation time series, and seismic hazard attributes through four components: entropy-regularized optimal transport for cross-modal semantic alignment without paired supervision; causally constrained hierarchical fusion enforcing domain-consistent modal weighting; scenario-based prototype mutation for semi-supervised learning from sparse expert annotations; and prototype-anchored variational graph clustering that simultaneously stratifies landslides into HIGH, MEDIUM, and LOW risk tiers and produces decomposed aleatoric and epistemic uncertainty estimates for operational triage. The HIGH risk tier operationally corresponds to predicted reactivation, validated against 598 documented reactivation events across 7482 co-seismic landslides from three Sichuan Province earthquake sequences: the 2013 Lushan (Mw 7.0), 2017 Jiuzhaigou (Mw 7.0), and 2022 Luding (Mw 6.8) events. PIMSEL achieves 82.5% reactivation recall and 66.4% precision, outperforming twelve baselines across clustering quality, classification, and uncertainty calibration metrics. Ablation studies confirm that optimal transport alignment contributes the largest individual performance gain. Current limitations include quarterly assessment frequency and dependence on optical imagery under cloud cover, which future integration of real-time meteorological triggers and SAR data should address.