Landslides are among the most frequent and destructive geological hazards worldwide. Accurate and timely detection of landslide-affected areas from high-resolution unmanned aerial vehicle (UAV) imagery is essential for disaster mitigation, emergency response, and post-event evaluation. Accurate landslide detection using high-resolution UAV imagery is crucial for disaster prevention and emergency response. However, complex terrain conditions, such as indistinct boundaries, scale variation and background interference from exposed rocks or shadows, make it difficult to extract reliable semantic features and achieve robust segmentation results. To address these challenges, we propose WSDLNet (Wavelet-guided Semantic Decoupling for Landslide Detection), a novel semantic segmentation framework that integrates wavelet-domain decomposition, hierarchical attention, and spatial-frequency decoupling. By leveraging frequency-aware encoding, adaptive multi-scale representation, and refined spatial attention, WSDLNet enhances feature discrimination in both spectral and spatial domains, enabling robust identification of landslide regions under diverse geological conditions. Extensive experiments on four practical UAV landslide datasets demonstrate that WSDLNet achieves state-of-the-art performance in terms of segmentation accuracy, boundary refinement, and generalization capability under diverse terrain and lighting conditions, offering a practical solution for intelligent landslide detection and risk assessment.

Link prediction is an important issue in graph data mining. In social networks, link prediction is used to predict missing links in current networks and new links in future networks. This process has a wide range of applications including recommender systems, spam mail classification, and the identification of domain experts in various research areas. In order to predict future node similarity, we propose a new model, Common Influence Set, to calculate node similarities. The proposed link prediction algorithm uses the common influence set of two unconnected nodes to calculate a similarity score between the two nodes. We used the area under the ROC curve (AUC) to evaluate the performance of our algorithm and that of previous link prediction algorithms based on similarity over a range of problems. Our experimental results show that our algorithm outperforms previous algorithms.